Everyone is talking about data science as the dream job that they want to have!
Yes, the “100K $USD annual package” is a big draw.
Furthermore, the key focus of self-help and self-improvement literature coming out in the last decade speak about doing what you enjoy and care about – in short, a job you love to do – since there is the greatest possibility that you will shine the brightest in those areas.
Hence many students and many adventurous challenge-hunting individuals from other professions and other (sometimes related) roles are seeking jobs that involve problem-solving. Data science is one solution since it offers both the chance to disrupt a company’s net worth and profits for the better by focusing on analytics from the data they already have as well as solving problems that are challenging and interesting. Especially for the math nerds and computer geeks with experience in problem-solving and a passionate thirst to solve their next big challenge.
So what can you do to land yourself in this dream role?
Fundamentals of Data Science
Data science comprises of several roles. Some involve data wrangling. Some involve heavy coding expertise. And all of them involve expert communication and presentation skills. If you focus on just one of these three aspects, you’re already putting yourself at a disadvantage. What you need is to follow your own passion. And then integrate it into your profession. That way you earn a high amount while still doing work you love to do, even at the level of going above and beyond all the expectations that your employer has of you. So if you’re reading this article, I assume that you are either a student who is intrigued by data science or a working professional who is looking for a more lucrative profession. In such a case, you need to understand what the industry is looking for.
From http://news.mit.edu/2018/mitx-micromasters-program-statistics-and-data-science
a) Coding Expertise
If you want to land a job in the IT or data science fields, understand that you will have to deal with code. Usually, that code will already have been written by some other people or company in the first place. So being intimate with programming and readiness to spend hours and hours of your life sitting before a computer and writing code is something you have to get used to. The younger you start, the better. Children pick up coding fastest compared to all other age groups so there is a very real use-case for getting your kids to code and to see if they seem to like it as young as possible. And there is not just coding – the best choices in these cases will involve people who know software engineering basics and even source control tools and platforms (like Git and GitHub) and have already started their career in coding by contributing to open source projects.
If you are a student, and you want to know what all the hype is about, I suggest that you visit a site that teaches programming – preferably in Python – and start developing your own projects and apps. Yes – apps. The IT world is now mobile, and anyone without knowledge of how to build a mobile app for his product will be left in the dust as far as the highest level of earningis concerned. Even deep learning frameworks, that were once academic, have migrated to the mobile and app ecosystem. That was unthinkable a mere five years ago. If you already know the basics of programming, then learn source control (Git), and how to build programs for open source projects. And then contribute to those projects while you’re still a student. In this case, you will actually become an individual that companies go hunting for before you even complete your schooling or college education. Instead of the other way around!
Mentoring
If you are a student or a professional who is interested in this domain, but don’t know where to start – well – the best thing to do is to find a mentor. You can define a mentor or a coach as someone who has achieved what you aim to achieve in your life. You learn from their experience, their networking capabilities, and their tough sides – the way to keep up your ambition and motivation when you feel the least motivated. If you want to learn data science, what better way than to learn from someone who has done that already? And you will gain a lot of traction when you show promise, especially on your networking side for job placement. For more on that topic (mentoring) – I highly recommend that you study the following article:
b) Cogent Communication (Writing and Speaking skills)
Even if you have the world’s best programming expertise, ACM awards, Mathematics Olympiad winning background, you name it – even if you are the best data scientist available in the industry today for your domain – you will go nowhere without communication skills. Communication is more than speaking, reading and typing English – it is the way you present yourself to others in the digital world. That is why blogging, content creation, and focused interaction with your target industry – say, on StackOverflow.com – are so important. A blog really resonates with those to whom you seek a job. It shows that you have genuine, original knowledge about your industry. And if your blog receives critical acclaim through several incoming links from the industry, expect a job interview offer in your email before too long. In many countries but especially in India, the market is flooded with graduates, postgraduates, and PhDs who might have top marks on paper but have no marketable skills as far as their job requirements demand.
Overcome your fears!
Right now it is difficult to see the difference between a 100th percentile skilled data scientist and a 30th percentile skill level by just looking at documents that you submit to a company. A blog testifies that you know your field authoritatively. It also means that you have gained attention from industry leaders (when you receive comments). A StackOverflow answer that is highly rated or even a mention in technology sites like GitHub indicate that you are an expert in your field. Communication is so critical that I recommend that you try to make the best use of every chance you get to speak in public. This is the window the world has on you. Make yourself heard. Be original. Be creative. And the best data scientist in the world will go nowhere unless he or she knows how to communicate effectively. In the industry, this capacity is known as soft skills. And it can be your single biggest advantage over the competition.If you are planning to join a training course for your dream job, make sure the syllabus covers it!
c) Social Networking and Building Industry Connections through LinkedIn
Many sources of information don’t focus on this issue, but it is an absolute must. Your next job could be waiting for you on LinkedIn through a connection. Studies show that less than 1% of resume submissions are selected for the final job offer and lucrative placement. But the same studies show that at least 30% of internal referrals from within a company get placed into the job of their dreams. Networking is important – so important that if you know the job you’re after, please reach out and research. Understand the company’s problems. Try to address some of their key issues. The more focused, you are the more likely it is that you will get placed in the company you aim for. But always have a plan B – a fallback system, so that in case you do not get placed, you will know what to do. This is especially important today with the competition being so intense.
The Facebook of the Workplace
One place where you can be noticed is through industry connections in social networks. You might miss this, even if you are an M.S. from a college in the US. LinkedIn profiles – the Facebook of the technology world – are especially important today. More and more, in an environment saturated with high-quality talent, who you know can sometimes be even more important as what you know. Connecting to professionals in the industry you plan to work in is critical. This can occur through meetups, through conferences, through technological symposiums and even through paid courses. Courses who have instructors with industry connections are worth their weight in gold – even platinum. Students of such courses who show outstanding promises will be directed to their industry leaders early. If you have a decent GitHub profile but don’t know where to go after that, one way is to go for a course with industry experienced experts. These are the people who are the most likely to be able to land you a job in such a competitive environment. Because the market for data scientists – in fact for IT professionals in general – is highly saturated, including locations like the US.
Conclusion
We have not covered all topics required on this issue, there is much more to speak about. You need to know Statistics – even at PhD levels sometimes, especially Inferential Statistics, Bayes Theorem, Probability and Analysis of Experiments. You should know Linear Algebra in-depth. Indeed, there is a lot to cover. But the best place to learn can be courses tailored to produce Data Scientists. Some firms have really gone the extra mile to convert industry knowledge and key results in each subtopic to create noteworthy training courses specially designed for data science students. In the end, no college degree alone will land you a dream job. What will land you a dream job is hard work and experience through internships and industry projects. Some courses like the ones offered by www.Dimensionless.in have resulted in stellar placement and guidance even after the course duration is finished and when you are a working professional in the job of your dreams. These courses offer –
Instructors with Industry Experience (not academic professors!)
It’s a simple yet potent formula to land you the job of your dreams. Compare the normal route to a data science dream job – a PhD from the US (starting cost Rs. 1,40,28,000.00 INR for five years total, as a usual range) – to a simple course at Rs. 50K to Rs. 25K (yes, INR) from the comfort of taking the course from wherever you may be in the world (remote but live tuition – not recorded videos) with a mic on your end to ask the instructor every doubt you have – and you have a remarkable product guaranteed to land you a dream job within six months. Think the offer’s too good to be true? Well; visit the link below, and pay special attention to the feedback from past students of these same courses on the home page.
Last words – you never know what the future holds – economy and convenience are both prudent and praiseworthy. All the best!
Data science is a booming industry, with potentially millions of job openings by 2020, according to the latest analyst’s business predictions. But what if you want to learn data science without the heavy cost of a postgraduate degree or the US university MOOC specialization? What is the best way to prepare for this upcoming wave of opportunity and maximize your chances for a 100K+ USD (annual) job? Well – there are many challenges that stand before you in such a case. Not only is the market saturated with an abundance of existing fresh talent, but most of the training you receive in college has no relationship to the actual type of work you get on the job. With so many engineering graduates passing out every year from so many established institutions such as the IITs, how can you hope to realistically compete? Well – there is one possibility you can choose if you wish to stand out from the rest of the competition – high-quality data science programs or courses. And in this article, we are going to list the top ten advantages of choosing such a course compared to other options, like a Ph.D., or an online MOOC Specialization from a US university (which are very tempting options, especially if you have the money for them).
Top Ten Advantages of Data Science Certification
1. Stick to Essentials, Cut the Fluff.
Now if you are a professional data scientist, no one expects you to derive any AI algorithms from first principles. You also don’t need to extensively dig into the (relatively) trivial history behind each algorithm, nor learn SVD (Singular Value Decomposition) or Gaussian Elimination on a real matrix without a computer to assist you. There is so much material that an academic degree covers that is never used on the job! Yes, you need to have an intuitive idea about the algorithms. But unless you’re going in for ML research, there’s not much use of knowing, say, Jacobians or Hessians in depth. Professional data scientists work in very different domains while compared to academic researchers or academic counterparts. Learn what you need on the job. If you try to cover everything mentioned in class, you’ve already lost the race. Focus on learning bare essentials thoroughly. You always have Google and StackOverflow to assist you as long as you’re not writing an exam!
2.Learning from Instructors with Work Experience, not PhD scientists!
Now from whom should you receive training? From PhD academics who’ve never worked on a real professional project but have published extensively, or instructors with real-life professional project experience? Very often, the teachers and instructors in colleges and universities belong to the former category, and you are remarkably fortunate if you have an instructor who has that invaluable component called industry experience. The latter category are rare and difficult to find, and you are lucky – even remarkably so – if you are studying under them. They will be able to teach you with context to the job experience in real-life, which is always exactly what you need the most.
3. Working with the Latest Technology Stacks.
Now, who would be better able to land you a job – teachers who teach what they studied ten years ago, or professionals who work with the latest tools available in the industry? It’s undoubtedly true that the people with industry experience can help you to choose what technologies you should learn and master. Academics, in comparison, could even be working with technology stacks over ten years old! Please try to stick with instructors who have work experience.
4. Individual Attention.
In a college or a MOOC with thousands of students, it’s simply not possible for each student to get individual attention. However, in data science programs, it is true that every student will receive individual attention tailored to their needs, which is exactly what you need. Every student is different and will have their own understanding of the projects available. This customized attention that is available when batch sizes are less than 30-odd is the greatest advantage such students have over college and MOOC students.
5. GitHub Project Portfolio Guidance.
Every college lecturer will advise you to develop a GitHub project portfolio, but they cannot give your individual profile genuine attention. The reason for that is that they have too many students and requirements upon their time to be able to spend time with individual project portfolios and actually mentor you in designing and establishing your own project portfolio. However, data science programs are different and it is genuinely possible for the instructors to mentor you individually in designing your project portfolios. Experienced industry professionals can even help you identify ‘niches’ within your fieldin which you can shine and carve out a special brand for your own project specialties so that you can really distinguish yourself and be a class apart from the rest of your competition.
6. Mentoring even After Getting Placed in a Company and Working by Yourself.
Trust me, no college professor will be able or even available to help you once you get placed within the industry since your domains will be so different. However, its a very different story with industry professionals who become instructors. You can even go to them or contact them for guidance even after placement, which is, simply not something most academic professors will be able to do unless they too have industry experience, which is very rare.
7. Placement Assistance.
People who have worked in the industry will know the importance of having company referrals in the placement process. It is one thing to have a cold call with a company with no internal referrals. Having someone already established within the company you apply to can be the difference between a successful and unsuccessful recruitment process. Every industry professional will have contacts in many companies, which puts them in a unique position to aid you at the time of placement opportunities.
8. Learn Critical but Non-Technical Job Skills, such as Networking, Communication, and Teamwork
teamwork in data science
While it is important to know the basics, one reason why brilliant students do badly in the industry after they get a job is the lack of soft skills like communication and teamwork. A job in the industry is so much more than bare skills studied in class. You need to be able to communicate effectively and to work well in teams, which can be guided by industry professionals but not by professors since they will have no experience in this area because they have never worked in the industry. Professionals will know who to guide you with regard to this aspect of your expertise, since its a case of being in that position and having learnt the necessary skills in the industry through their job experiences and work capacities.
9. Reduced Cost Requirements
It is one thing to be able to sponsor your own PhD doctoral fees. It is quite another thing to learn the very same skills for less than 1% of the cost of a PhD degree in, say, the USA. Not only is it financially less demanding, but you also don’t have to worry about being able to pay off massive student loans through industry work and fat paychecks, often at the cost of compromising your health or your family needs. Why take a Rs. 75 lakh student loan, when you can get the same outcome from a course less than 0.5% of the price? The takeaways will still be the same! In most cases, you will even receive better training through the data science program than an academic qualification because your instructors will have job experience.
10. Highly Reduced Time Requirements
A PhD degree takes, on average, 5 years. A data science program gets you job-ready in a few months time. Why don’t you decide which is better for you? This is especially true when you already have job experience in another domain or you are more than 23-25 years old, and doing a full PhD program could put you on the wrong side of 30 with almost no job experience. Please go for the data science program, since the time spent working in your 20s is critical for most companies who are hiring today since they consider you to a be a good ‘çultural fit’ for the company environment, especially when you have less than 3-4 years experience.
Summary
Thus, its easy to see that in so many ways, a data science program can be much better for you than a data science degree. So, the critical takeaway for this article is that there is no need to spend Rs. 75,000,000+ for skills which you can acquire for Rs. 35,000 max. It really is a no-brainer. These data science programs really offer true value for money. In case you’re interested, please do check out the following data science programs, each of which have every one of the advantages listed above:
In a 2017 business research article IBM predicted that the need for Data Scientists will increase by 28% by 2020, with nearly 3 million job openings for Data Science professionals. According to a Forbes report, Data Science is the best job in America for three consecutive years, with a median base salary of $110,000 and over 4,524 job openings.
According to Glassdoor’s 50 Best Jobs In America For 2018 research, Data Scientist jobs are among the 50 best jobs based on each job’s overall Glassdoor Job Score. We calculate the Glassdoor Job Score by weighing three key factors equally: earning potential based on the median annual base salary, job satisfaction rating, and the number of job openings. Hence, the need for sharpening Data Scientist skills are at an all-time high.
In this blog, we will be looking at all the technical and non-technical skills that are absolute in mastering the domain of data science.
Technical Skills
R & Python
R is a language for statistical computations, data analysis and graphical representation of data. It is a very popular language in academia. Many researchers and scholars use it for experimenting with data science. Many popular books and learning resources on data science use R for statistical analysis as well. Also, it has an extensive library of tools for database manipulation and wrangling. Data visualization is the visual representation of data in graphical form. This allows analyzing data from angles which are not clear in unorganized or tabulated data. R has many tools that can help in data visualization, analysis, and representation. The packages ggplot2 and ggedit for have become the standard plotting packages. Also, It allows practising a wide variety of statistical and graphical techniques like time-series analysis, classification, classical statistical tests, clustering, etc.
When it comes to data science, Python is a very powerful tool, which is also open sourced and flexible, adding more to its popularity. It has massive libraries for manipulation of data and is extremely easy to learn and use for all data analysts. Anyone who is familiar with programming languages such as, Java, Visual Basic, C++ or C, will find this tool to be very accessible and easy to work with. Apart from being an independent platform, this tool has the ability to easily integrate with the existing Infrastructure and can also solve the most difficult of problems. This tool is powerful, friendly, easy and plays well with others, apart from running everywhere. A lot of banks use this tool for the purpose of crunching data, some institutions use it for analyzing and visualization. This tool offers the great benefit of using one programming language, across multiple application platforms.
Python has already been proven to be as good as R Programming is, in terms of all the process under data analytics. Any novice, entering the field of data analytics can use this programming language to start in the data science industry. As a result of its multipurpose uses, there are a lot of institutes, which offer courses in Python.
Hadoop
Hadoop is an open-source software framework that provides for processing of large data sets across clusters of computers using simple programming models. It can scale up from single servers to thousands of machines.
Hadoop grew out of an open-source search engine called Nutch, developed by Doug Cutting and Mike Cafarella. Back in the early days of the Internet, the pair were looking forward to inventing a way to return web search results faster by distributing data and calculations across different computers so multiple tasks could execute at the same time.
It has a lot to offer. Benefits are :
Computing power: Hadoop’s distributed computing model allows it to process huge amounts of data. The more nodes you use, the more processing power you have.
Flexibility: Hadoop stores data without requiring any preprocessing. Store data — even unstructured data such as text, images, and video — now; decide what to do with it later.
Fault tolerance: Hadoop automatically stores multiple copies of all data, and if one node fails during data processing, jobs are redirected to other nodes and distributed computing continues.
Low cost: The open-source framework is free, and data is stored on commodity hardware.
Scalability: You can easily grow your Hadoop system, simply by adding more nodes.
Although the development of Hadoop came from the need to search millions of web pages and return relevant results, it today serves a variety of purposes. Hadoop’s low-cost storage makes it an appealing option for storing information that is not currently critical but that might be analyzed later.
Spark
Hadoop continues to garner the most name-recognition in big data processing, but Spark is, appropriately, beginning to ignite it’s utility as a vehicle for data analysis and processing, versus simply data storage.
It consists of four core components:
Hadoop Common — Essential utilities and tools referenced by the other modules
Distributed File System — The high-throughput file storage system (HDFS)
Hadoop YARN — The job-scheduling framework for distributed process allocation
MapReduce — The parallel processing module based on YARN
Spark replaces only two of those, YARN and MapReduce. According to a February 2016 article in Information Week, many Spark implementations chug happily away on top of Hadoop Common code and the HDFS. Thanks to the integration, many major companies that have implemented Hadoop clusters to deal with insane amounts of data — the likes of Amazon and Facebook — have kept the data storage elements and simply swapped in Spark as a high-performance alternative to MapReduce.
SQL
SQL, or Structured Query Language, is a special-purpose programming language for managing data held in relational database management systems. Almost all structured data resides in such databases, so, if you want to play with data, chances are you’ll want to know some SQL.
Here are some awesome things you can do with SQL
Generate queries from a query: Basic string concatenation makes it easy to generate en masse queries that use data in a database to fetch data found in another system.
Handle dates: “Fantastic date functions” exist to meet all your formatting and type conversion needs.
Text mining: Yhat recommends going as far as you can with SQL’s built-in string functions before turning to a scripting language.
Find the median: Since there’s no built-in aggregate function for median, Yhat provides the code.
Load data into your database with the \COPY command.
Generate sequences: Use the generate_series function to create ranges of dates and times and to handle time series and funnels.
Machine Learning
Simply put, Machine Learning is the core subarea of artificial intelligence. It makes computers get into a self-learning mode without explicit programming. When fed new data, these computers learn, grow, change, and develop by themselves.
The machine learning field is constantly evolving. And along with evolution comes a rise in the demand and importance. There is one crucial reason why data scientists need machine learning, and that is: ‘High-value predictions that can guide better decisions and smart actions in real time without human intervention’.
Machine learning as a technology helps analyze large chunks of data, easing the tasks of data scientists in an automated process and is gaining a lot of prominence and recognition. Machine learning has changed the way data extraction and interpretation works by involving automatic sets of generic methods that have replaced the traditional statistical techniques.
Non-Technical Skills
Now, the skill set of a successful data scientist will comprise both technical and non-technical skills. While technical skills like programming and quantitative analysis are important, it is easy to undervalue the impact of non-technical skills. So, before we go on to the technical stuff, here is a list of 5 non-technical skills that you must possess:
Communication
Effective business communication is one of the most important abilities. Whether it’s understanding the business requirements or the problem at hand, seeking more data from stakeholders or communicating insights, a data scientist needs to be convincing. ” Storytelling, ” as data scientists call it, means that analytical solutions are communicated in a clear, concise and timely manner in order to benefit both technical and non-technical people. Data visualization and presentation tools are widely employed by data scientists for their graphic appeal and easy absorption by all teams in the organization. Often underestimated, this is one of the most important skills for the simple reason that all statistical computation is useless if the teams can’t act upon it.
Data-Driven Decision Making
A data scientist will not conclude, judge, or decide without adequate data. Scientists need to decide their approach to a business problem in addition to deciding several other things like where to look, what tools and techniques to use, and how to visualize and communicate it in the most effective possible way. The most important thing for them is to ask relevant questions, even if they seem far-fetched. Think of it as a child exploring all his surroundings to draw conclusions. A data scientist is pretty much the same.
Mathematical and Statistical Acumen
A data scientist will never thrive if he/she doesn’t understand what test to run when and how to interpret their findings. They need a solid understanding of algebra and calculus. In good old days, Math was a subject based on common sense and the need to resolve basic problems based on logic. This hasn’t changed much, though the scale has blown up exponentially. A statistical sensibility provides a solid foundation for several analysis tools and techniques, which are used by a data scientist to build their models and analytic routines.
Teamwork
Teamwork is another feather in the cap that data scientists can not do without. Although they may appear to be able to work in isolation, they are closely involved in the organization at various levels. On the one hand, they will have to work with the teams to understand their requirements, collect feedback to achieve beneficial solutions, and on the other hand work with data scientists, data architects and data engineers to perform their tasks well. The culture in a data-driven organization will never be that of the data science team working in isolation; instead, the team will have to use the same characteristics across the organization to make the best use of the insights they draw from various departments.
Intellectual Curiosity and Passion
This is a tad-bit cliched but true. Data scientists are passionate about their work and have an inconsolable itch to use data to find patterns and provide solutions to business problems. They often have to work with unstructured data and rarely know the exact steps they need to take to find valuable insights that lead to business growth. Sometimes, they don’t even have a clear problem to work with, just signs that there is something wrong. That’s where their intellectual curiosity guides them to look in areas no one else has looked in. You don’t need to read “How to think like Sherlock,” just ask a data scientist!
Conclusion
The next question I always get is, “What can I do to develop these skills?” There are many resources around the web, but I don’t want to give anyone the mistaken impression that the path to data science is as simple as taking a few MOOCs. Unless you already have a strong quantitative background, the road to becoming a data scientist will be challenging but not impossible.
However, if it’s something you sincerely want to pursue and have a passion for data and lifelong learning, don’t let your background discourage you from pursuing data science as a career.
A decade ago, machine learning was simply a concept but today it has changed the way we interact with technology. Devices are becoming smarter, faster and better, with Machine Learning at the helm.
Thus, we have designed a comprehensive list of projects in Machine Learning course that offers a hands-on experience with ML and how to build actual projects using the Machine Learning algorithms. Furthermore, this course is a follow up to our Introduction to Machine Learning course and delves further deeper into the practical applications of Machine Learning.
Progressing step by step
In this blog, we will have a look at projects divided mostly into two different levels i.e. Beginners and Advanced. First, projects mentioned under the beginner heading cover important concepts of a particular technique/algorithm. Similarly, projects under advanced category involve the application of multiple algorithms along with key concepts to reach the solution of the problem at hand.
Projects offered by Dimensionless Technologies
We have tried to take a more exciting approach to Machine Learning, by not working on simply the theory of it, but instead by using the technology to actually build real-world projects that you can use. Furthermore, you will learn how to write the codes and then see them in action and actually learn how to think like a machine learning expert.
Following are some of the projects among many others that they cover in their courses:
Disease Detection — In this project, you will use the K-nearest neighbor algorithm to help detect breast cancer malignancies by using a support vector machine.
Credit Card Fraud Detection — In this project, you are going to do a credit card fraud detection and going to focus on anomaly detection by using probability densities.
Stock Market Clustering Project — In this project, you will use a K-means clustering algorithm to identify related companies by finding correlations among stock market movements over a given time span.
Beginners
1) Iris Flowers Classification ML Project– Learn about Supervised Machine Learning Algorithms
Iris flowers dataset is one of the best data sets in classification literature. The classification of the iris flowers machine learning project is often referred to as the “Hello World” of machine learning. Furthermore, this dataset has numeric attributes and beginners need to figure out how to load and handle data. Also, the iris dataset is small which easily fits into the memory and does not require any special transformations or scaling, to begin with.
The goal of this machine learning project is to classify the flowers into among the three species — virginica, setosa, or versicolor based on length and width of petals and sepals.
2) Social Media Sentiment Analysis using Twitter Dataset
Platforms like Twitter, Facebook, YouTube, Reddit generate huge amounts of big data that can be mined in various ways to understand trends, public sentiments, and opinions. A sentiment analyzer learns about various sentiments behind a “content piece” through machine learning and predicts the same using AI. Also, Twitter data is considered a definitive entry point for beginners to practice sentiment analysis. Hence, using Twitter dataset, one can get a captivating blend of tweet contents and other related metadata such as hashtags, retweets, location and more which pave way for insightful analysis. Using Twitter data you can find out what the world is saying about a topic whether it is movies, sentiments about any trending topic. Probably, working with the Twitter dataset will help you understand the challenges associated with social media data mining and also learn about classifiers in depth.
3) Sales Forecasting using Walmart Dataset
Walmart dataset has sales data for 98 products across 45 outlets. Also, the dataset contains sales per store, per department on weekly basis. The goal of this machine learning project is to forecast sales for each department in each outlet consequently which will help them make better data-driven decisions for channel optimization and inventory planning. Certainly, the challenging aspect of working with Walmart dataset is that it contains selected markdown events which affect sales and should be taken into consideration.
In the book Moneyball, the Oakland A’s revolutionized baseball through analytical player scouting. Furthermore, they built a competitive squad while spending only 1/3 of what large market teams like the Yankees were paying for salaries.
First, if you haven’t read the book yet, you should check it out. Ceratinly, It’s one of our favorites!
Fortunately, the sports world has a ton of data to play with. Data for teams, games, scores, and players are all tracked and freely available online.
There are plenty of fun machine learning projects for beginners. For example, you could try…
Sports Betting… Predict box scores given the data available at the time right before each new game.
Talent scouting… Use college statistics to predict which players would have the best professional careers.
General managing… Create clusters of players based on their strengths in order to build a well-rounded team.
Sports is also an excellent domain for practicing data visualization and exploratory analysis. You can use these skills to help you decide which types of data to include in your analyses.
Data Sources
Sports Statistics Database — Sports statistics and historical data covering many professional sports and several college ones. The clean interface makes it easier for web scraping.
Sports Reference — Another database of sports statistics. More cluttered interface, but individual tables can be exported as CSV files.
cricsheet.org — Ball-by-ball data for international and IPL cricket matches. CSV files for IPL and T20 internationals matches are available.
As the name suggests (no points for guessing), this dataset provides the data on all the passengers who were aboard the RMS Titanic when it sank on 15 April 1912 after colliding with an iceberg in the North Atlantic ocean. Also, it is the most commonly used and referred to data set for beginners in data science. With 891 rows and 12 columns, this data set provides a combination of variables based on personal characteristics such as age, class of ticket and sex, and tests one’s classification skills.
Objective: Predict the survival of the passengers aboard RMS Titanic.
Advance level projects
This is where an aspiring data scientist makes the final push into the big leagues. After acquiring the necessary basics and honing them in the first two levels, it is time to confidently play the big game. Certainly, these datasets provide a platform for putting to use all the learnings and take on new, and more complex challenges.
This data set is a part of the Yelp Dataset Challenge conducted by crowd-sourced review platform, Yelp. It is a subset of the data of Yelp’s businesses, reviews, and users, provided by the platform for educational and academic purposes.
In 2017, the tenth round of the Yelp Dataset Challenge was held and the data set contained information about local businesses in 12 metropolitan areas across 4 countries.
Rich data comprising 4,700,000 reviews, 156,000 businesses, and 200,000 pictures provides an ideal source of data for multi-faceted data projects. Projects such as natural language processing and sentiment analysis, photo classification, and graph mining among others, are some of the projects that can be carried out using this dataset containing diverse data. The data set is available in JSON and SQL formats.
Objective: Provide insights for operational improvements using the data available.
With the increasing demand to analyze large amounts of data within small time frames, organizations prefer working with the data directly over samples. Consequently, this presents a herculean task for a data scientist with a limitation of time.
This dataset contains information on reported incidents of crime in the city of Chicago from 2001 to the present. It does not contain data from the most recent seven days. Not included in the data set, is data on murder, where data is recorded for each victim.
It contains 6.51 million rows and 22 columns and is a multi-classification problem. In order to achieve mastery over working with abundant data, this dataset can serve as the ideal stepping stone.
Objective: Explore the data, and provide insights and forecasts about crimes in Chicago.
KKD cup is a popular data mining and knowledge discovery competition held annually. It is one of the first-ever data science competition which dates back to 1997.
Every year, the KDD cup provides data scientists with an opportunity to work with data sets across different disciplines. Some of the problems tackled in the past include
Identifying which authors correspond to the same person
Predicting the click-through rate of ads using the given query and user information
Development of algorithms for Computer Aided Detection (CAD) of early-stage breast cancer among others.
The latest edition of the challenge was held in 2017 and required participants to predict the traffic flow through highway tollgates.
Objective: Solve or make predictions for the problem presented every year.
Conclusion
Undertaking different kinds of projects is one of the good ways through which one can progress in any field. Certainly, this allows an individual to have hands on at the problems faced during the implementation phase. Also, it is easier to learn concepts by applying them. Finally, you will have a feeling of doing actual work rather than just being all lost in the theoretical part.
There are wonderful competitions available on kaggle and other similar data science competition platforms. Hence, make sure you take some time out and jump into these competitions. Whether you are a beginner or a pro, certainly, there is a lot of learning available while attempting these projects.
Although Data Science has been around us ever since the 1960s, it has only gained traction in the last few decades. This is one of the main reasons why budding data scientists find it quite challenging to find the right mentors. However, this scenario is drastically changing now. With the right approach and by looking at the right corners, you can find data scientist mentors who can help you bridge the gap between theoretical and practical applications of data science.
In this article, we will be looking at why there is even a need for individuals to have mentors in data science and how can we find them.
Why does one need a mentor in Data Science?
Earlier for data science jobs if you had a technical grad degree, you could brush up on your Python skills, fill a small portfolio with scikit-learn projects, and more or less watch the offers roll in. But this is not the case anymore. Data science industry has made a lot of advancements in a small span of time. These advancements have basically done two things here. First, they have resulted in organizations looking for more than basic skills from the data scientists. Secondly, it has created a huge demand for data scientists which have resulted in a lot of competition between different job seekers
Take you under their wing and help you to stay motivated and discover the path that you may need to take.
Understand what it takes to get to the top and be a valuable resource by answering your career or work related questions and providing good advice.
Help you to be passionate about your success and brand.
Provide you with a wealth of knowledge and resources and help you to connect with various Subject Matter Experts (SMEs).
Be your own personal cheerleader and help you discover new opportunities.
Where to find mentors?
Available Online Courses
Dimensionless Technologies provides best online data science training that provides in-depth course coverage, case study based learning, entirely Hands-on driven sessions with personalized attention to every participant. We provide only instructor-led LIVE online training sessions and not classroom training.
These programs are led by Kushagra (IIT, Delhi – 10+ years experience in Data Science) who is a machine-learning practitioner, fascinated by the numerous application of Artificial Intelligence in the day to day life. He enjoys applying my quantitative skills to new large-scale, data-intensive problems. Also, he is an avid learner, keen to be the frontrunner in the field of AI.
LinkedIn
Learning data science will never be easy without any help from the community or from someone who is willing to help beginners. These someones are the ones that are making up our amazing LinkedIn Data Science Community.
Kate Strachnyi ♕ – If you’re in pursuit of having a data science career and learning data visualization. You must follow her in a heartbeat. Her hashtag#makerovermonday posts are amazing and she shares a lot of information that can help you in your journey.
Randy Lao ☁️ – He has been serving the community from as long as I know there was one for data science in LinkedIn. He shares the best resources for everything in data science. Starting from libraries in Python to courses on machine learning. You will find a lot of useful resources in his posts. Also, check out his collaboration with Kyle.
Kyle McKiou – Top-notch data science influencer. Can’t afford to not follow him if you want to learn from books to pick for data science to interview tips and tricks. Constantly helping beginners with his experience and content.
Mentors at work/college
Experienced working with professional developers can make or break your ability to land a data science position.
The best strategy we’ve found is called income share: basically, aspiring data scientists work with an expert mentor on an industry-level project, and they pay their mentor a small share of their future income in exchange (but only if they actually get hired as a result).
Income share has two benefits: first, it means that you can get expert instruction at no upfront cost. You only pay when you can afford to, and if you don’t get a data science job within a certain time limit (usually 24 months) you don’t pay anything at all.
Second, income share aligns the incentives of the mentors and mentees. Even after the formal mentorship period ends, mentors still have a stake in your future success, which means they’ll look out for opportunities for you by default.
Income share mentorships make new opportunities accessible to people who can’t afford the expert time or find professional data scientists to learn from.
Paid mentorship available at different websites
If you have a disposable income to spend then I’d highly recommend hiring a mentor who can walk you through your problems. When I was just starting to learn data science, I found having a paid mentor (via Thinkful) was incredibly helpful as it allowed me to ask all the dumb questions that I otherwise would’ve been too embarrassed to ask on a community forum.
Learn code & data science 1-on-1 with a mentor
Instant hands-on programming help available 24/7
Clarity – On Demand Business Advice
What if you are not able to find one?
Take ownership of your career
People who think a mentor is a key to success may lack confidence in their own ability to take initiative. Carreau advises taking control of your own growth. “Create your own plan to take control of your career and your life, and rise above the average,” she recommends.”Howard Schultz is a great example of someone from humble beginnings who took control of his destiny early. He has famously said, ‘I had no mentor, no role model, no special teacher to help me sort out my options.
Get value from peers
Even though a mentor is not necessary to success does not mean that you should ignore the opportunity of turning to your peers for guidance. In fact, Carreau advises joining a peer-mentoring group. “The combination of great networking and feedback makes participation in these kinds of circles invaluable, rather than the standard networking coffee meetings between two people,” she stated. “Getting feedback from not just one, but many people is much more valuable, and leads to better solutions and ideas. The dialogue in these groups consistently impresses me. And the business outcomes I see from promotions to career changes to overcoming major career challenges are substantial.
Learn from the youth
As technology continues to accelerate the rate of change, many would-be mentors are finding their approach outdated and obsolete. They find they actually have a lot to learn from younger generations. Instead of using experienced senior leaders as mentors to younger colleagues, some companies are reversing roles. “The younger person becomes the mentor, and the senior professional becomes the mentee,” explains Carreau. “In India, they have found that this concept has re-energized senior professionals and showed them a lot about technology and the social media world we live in today.
Combine activities to maximize time
For real value to take place, mentorship requires focused time, which is a valuable commodity. Carreau recommends looking for ways to combine learning activities to save time and be productive all at once. “Instead of going for a jog and then meeting a friend for coffee, why not go for a jog with your friend?” notes Carreau. “Instead of letting your commute be wasted time, listen to a podcast, relevant news or language tapes. Leveraging the power of multipliers lets you accomplish more by overlaying tasks that make sense together.”
Become a better networker
Building a network is not an intuitive skill for most people. It is also an iterative process; you are never finished, and the way you develop your network will change as your career progresses. When you begin networking, you are still figuring out your interests and career goals,” elaborates Carreau. Because of this, you must cast as large a net as possible among the people you can. Hence you should contact-family friends, school alumni and more. As you understand your ambitions better, you will become a better networker as well. Thus you are able to quickly spot the diamond in the rough among your contacts. You should not focus on networking and wasting your time on superficial contacts. Rather, make ensure you are engaging in an authentic and helpful way with them both online and in person.
Conclusion
Data science is one of the areas where this idea is starting to take off. Data scientists remain rare, and students may find it hard to get access to information. Mentoring bridges that gap and enables students to improve their skills and understanding of using data science in business.
Never thought that online trading could be so helpful because of so many scammers online until I met Miss Judith... Philpot who changed my life and that of my family. I invested $1000 and got $7,000 Within a week. she is an expert and also proven to be trustworthy and reliable. Contact her via: Whatsapp: +17327126738 Email:judithphilpot220@gmail.comread more
A very big thank you to you all sharing her good work as an expert in crypto and forex trade option. Thanks for... everything you have done for me, I trusted her and she delivered as promised. Investing $500 and got a profit of $5,500 in 7 working days, with her great skill in mining and trading in my wallet.
judith Philpot company line:... WhatsApp:+17327126738 Email:Judithphilpot220@gmail.comread more
Faculty knowledge is good but they didn't cover most of the topics which was mentioned in curriculum during online... session. Instead they provided recorded session for those.read more
Dimensionless is great place for you to begin exploring Data science under the guidance of experts. Both Himanshu and... Kushagra sir are excellent teachers as well as mentors,always available to help students and so are the HR and the faulty.Apart from the class timings as well, they have always made time to help and coach with any queries.I thank Dimensionless for helping me get a good starting point in Data science.read more
My experience with the data science course at Dimensionless has been extremely positive. The course was effectively... structured . The instructors were passionate and attentive to all students at every live sessions. I could balance the missed live sessions with recorded ones. I have greatly enjoyed the class and would highly recommend it to my friends and peers.
Special thanks to the entire team for all the personal attention they provide to query of each and every student.read more
It has been a great experience with Dimensionless . Especially from the support team , once you get enrolled , you... don't need to worry about anything , they keep updating each and everything. Teaching staffs are very supportive , even you don't know any thing you can ask without any hesitation and they are always ready to guide . Definitely it is a very good place to boost careerread more
The training experience has been really good! Specially the support after training!! HR team is really good. They keep... you posted on all the openings regularly since the time you join the course!! Overall a good experience!!read more
Dimensionless is the place where you can become a hero from zero in Data Science Field. I really would recommend to all... my fellow mates. The timings are proper, the teaching is awsome,the teachers are well my mentors now. All inclusive I would say that Kush Sir, Himanshu sir and Pranali Mam are the real backbones of Data Science Course who could teach you so well that even a person from non- Math background can learn it. The course material is the bonus of this course and also you will be getting the recordings of every session. I learnt a lot about data science and Now I find it easy because of these wonderful faculty who taught me. Also you will get the good placement assistance as well as resume bulding guidance from Venu Mam. I am glad that I joined dimensionless and also looking forward to start my journey in data science field. I want to thank Dimensionless because of their hard work and Presence it made it easy for me to restart my career. Thank you so much to all the Teachers in Dimensionless !read more
Dimensionless has great teaching staff they not only cover each and every topic but makes sure that every student gets... the topic crystal clear. They never hesitate to repeat same topic and if someone is still confused on it then special doubt clearing sessions are organised. HR is constantly busy sending us new openings in multiple companies from fresher to Experienced. I would really thank all the dimensionless team for showing such support and consistency in every thing.read more
I had great learning experience with Dimensionless. I am suggesting Dimensionless because of its great mentors... specially Kushagra and Himanshu. they don't move to next topic without clearing the concept.read more
My experience with Dimensionless has been very good. All the topics are very well taught and in-depth concepts are... covered. The best thing is that you can resolve your doubts quickly as its a live one on one teaching. The trainers are very friendly and make sure everyone's doubts are cleared. In fact, they have always happily helped me with my issues even though my course is completed.read more
I would highly recommend dimensionless as course design & coaches start from basics and provide you with a real-life... case study. Most important is efforts by all trainers to resolve every doubts and support helps make difficult topics easy..read more
Dimensionless is great platform to kick start your Data Science Studies. Even if you are not having programming skills... you will able to learn all the required skills in this class.All the faculties are well experienced which helped me alot. I would like to thanks Himanshu, Pranali , Kush for your great support. Thanks to Venu as well for sharing videos on timely basis...😊
I highly recommend dimensionless for data science training and I have also been completed my training in data science... with dimensionless. Dimensionless trainer have very good, highly skilled and excellent approach. I will convey all the best for their good work. Regards Avneetread more
After a thinking a lot finally I joined here in Dimensionless for DataScience course. The instructors are experienced &... friendly in nature. They listen patiently & care for each & every students's doubts & clarify those with day-to-day life examples. The course contents are good & the presentation skills are commendable. From a student's perspective they do not leave any concept untouched. The step by step approach of presenting is making a difficult concept easier. Both Himanshu & Kush are masters of presenting tough concepts as easy as possible. I would like to thank all instructors: Himanshu, Kush & Pranali.read more
When I start thinking about to learn Data Science, I was trying to find a course which can me a solid understanding of... Statistics and the Math behind ML algorithms. Then I have come across Dimensionless, I had a demo and went through all my Q&A, course curriculum and it has given me enough confidence to get started. I have been taught statistics by Kush and ML from Himanshu, I can confidently say the kind of stuff they deliver is In depth and with ease of understanding!read more
If you love playing with data & looking for a career change in Data science field ,then Dimensionless is the best... platform . It was a wonderful learning experience at dimensionless. The course contents are very well structured which covers from very basics to hardcore . Sessions are very interactive & every doubts were taken care of. Both the instructors Himanshu & kushagra are highly skilled, experienced,very patient & tries to explain the underlying concept in depth with n number of examples. Solving a number of case studies from different domains provides hands-on experience & will boost your confidence. Last but not the least HR staff (Venu) is very supportive & also helps in building your CV according to prior experience and industry requirements. I would love to be back here whenever i need any training in Data science further.read more
It was great learning experience with statistical machine learning using R and python. I had taken courses from... Coursera in past but attention to details on each concept along with hands on during live meeting no one can beat the dimensionless team.read more
I would say power packed content on Data Science through R and Python. If you aspire to indulge in these newer... technologies, you have come at right place. The faculties have real life industry experience, IIT grads, uses new technologies to give you classroom like experience. The whole team is highly motivated and they go extra mile to make your journey easier. I’m glad that I was introduced to this team one of my friends and I further highly recommend to all the aspiring Data Scientists.read more
It was an awesome experience while learning data science and machine learning concepts from dimensionless. The course... contents are very good and covers all the requirements for a data science course. Both the trainers Himanshu and Kushagra are excellent and pays personal attention to everyone in the session. thanks alot !!read more
Had a great experience with dimensionless.!! I attended the Data science with R course, and to my finding this... course is very well structured and covers all concepts and theories that form the base to step into a data science career. Infact better than most of the MOOCs. Excellent and dedicated faculties to guide you through the course and answer all your queries, and providing individual attention as much as possible.(which is really good). Also weekly assignments and its discussion helps a lot in understanding the concepts. Overall a great place to seek guidance and embark your journey towards data science.read more
Excellent study material and tutorials. The tutors knowledge of subjects are exceptional. The most effective part... of curriculum was impressive teaching style especially that of Himanshu. I would like to extend my thanks to Venu, who is very responsible in her jobread more
It was a very good experience learning Data Science with Dimensionless. The classes were very interactive and every... query/doubts of students were taken care of. Course structure had been framed in a very structured manner. Both the trainers possess in-depth knowledge of data science dimain with excellent teaching skills. The case studies given are from different domains so that we get all round exposure to use analytics in various fields. One of the best thing was other support(HR) staff available 24/7 to listen and help.I recommend data Science course from Dimensionless.read more
I was a part of 'Data Science using R' course. Overall experience was great and concepts of Machine Learning with R... were covered beautifully. The style of teaching of Himanshu and Kush was quite good and all topics were generally explained by giving some real world examples. The assignments and case studies were challenging and will give you exposure to the type of projects that Analytics companies actually work upon. Overall experience has been great and I would like to thank the entire Dimensionless team for helping me throughout this course. Best wishes for the future.read more
It was a great experience leaning data Science with Dimensionless .Online and interactive classes makes it easy to... learn inspite of busy schedule. Faculty were truly remarkable and support services to adhere queries and concerns were also very quick. Himanshu and Kush have tremendous knowledge of data science and have excellent teaching skills and are problem solving..Help in interviews preparations and Resume building...Overall a great learning platform. HR is excellent and very interactive. Everytime available over phone call, whatsapp, mails... Shares lots of job opportunities on the daily bases... guidance on resume building, interviews, jobs, companies!!!! They are just excellent!!!!! I would recommend everyone to learn Data science from Dimensionless only 😊read more
Being a part of IT industry for nearly 10 years, I have come across many trainings, organized internally or externally,... but I never had the trainers like Dimensionless has provided. Their pure dedication and diligence really hard to find. The kind of knowledge they possess is imperative. Sometimes trainers do have knowledge but they lack in explaining them. Dimensionless Trainers can give you ‘N’ number of examples to explain each and every small topic, which shows their amazing teaching skills and In-Depth knowledge of the subject. Himanshu and Kush provides you the personal touch whenever you need. They always listen to your problems and try to resolve them devotionally.
I am glad to be a part of Dimensionless and will always come back whenever I need any specific training in Data Science. I recommend this to everyone who is looking for Data Science career as an alternative.
All the best guys, wish you all the success!!read more
