A career in data science is hyped as the hottest job of the 21st century, but how do you become a data scientist? How should you, as an aspiring data scientist, or a student who aims at a data science job, prepare? What are the skills you need? What must you do? Fret not – this article will answer all your questions and give you links with which you can jump-start a new career in data science!
Data science as a field is a cross-disciplinary topic. By this, we mean that the data scientist has to know multiple fields and be an expert in many different things. A data scientist must have a strong foundation in the following subjects:
The following diagram shows a little bit of the subjects you will need to master to become a high-quality data scientist:
Now unless you have been focused like a laser beam and have deliberately focused your studies in these areas, it is likely that you will not know one or more of the topics given above. Or you may know two or three really well but may not be solid in the rest. For example, you could be a computer science student who knows mathematics but not statistics to the in-depth level that Analysis of Statistical Research requires. Or you could be a statistician who has a little foundation in programming.
But there are ways to get past that crucial job interview. The five things you must do are:
There is no substitute for industry experience. If your instructor is not just an enthusiastic amateur (as in the case of many courses available online) but someone with 5+ years of industry experience working in the data science industry, you have the best possible trainers in the field. It is one thing to learn Python and R. It is quite a completely different thing to master Python and R. If you want to do well in the industry, mastery is required, not just basic abilities. Make sure your faculty members have verified industry experience. Because that experience is what will count in finally landing you a job in a top-notch data science company. You will always learn the most from experts who have industry experience rather than academics who have a Ph.D. even in the subject but have not worked in the field.
Having an online portfolio in GitHub is critical!
All the best training in the field will take you nowhere if you don’t code what you learn and apply the lessons to real-life datasets and scenarios. You need to do data science projects. Try to make your projects as attractive as possible. As much as you can, your GitHub project portfolio should be built with these guidelines in mind:
Kaggle.com is your training arena.
If you are into data science, become a Kaggler immediately! Or, if your taste leans more towards development, join TopCoder (they also have data science tracks). Kaggle is widely touted as the home of data science and for good reason, since Kaggle has been hosting data science competitions for many years and is the international location of all the best data science competitions. One of the simplest ways to get a call from a reputed company is to rank as high as possible on Kaggle. What is more, you will be able to compare your performance with the top competition in the industry.
There are plenty of sites available online that have excellent collections of industry questions used in data science interviews. Now, no one expects you to mug up 200 interview questions, but they do expect you to be able to solve basic data science and algorithm questions in code (Python preferably) or in pseudocode. You also need to know basic concepts like what cross-validation is, the curse of dimensionality, and the problem of overfitting and how you deal with it in practice in real-world scenarios. You should also be able to explain the internal details of most data science algorithms, for example, AdaBoost. Knowledge of linear algebra, statistics, and some basic multivariable calculus is also required to possess that extra edge over the competition.
This may not seem connected with data science, but it is a fundamental component in any job search. What is the first thing that a prospective employer looks for while hunting for job candidates, when given a name? That’s right – he’ll Google it first. What comes up when you Google your name? Is your online profile safe under scrutiny? That is:
If the answers to any of these questions are no, you may need to adjust or tweak your online profile. You can do this by blog posts, informed mature comments online, or even creating a blog for yourself and speaking about yourself to the world in a positive manner. This is critical for any job applicant today, in this online, digital, connected world.
You are trying to sell yourself and your credibility online to people who have never seen you, and not even heard your name. Your Internet profile will make the key crucial difference here, to make sure you stand out from the competition. Many training sites are available that offer courses by amateurs or people with less than 2 years of industry experience. Don’t make the unwise choice to be satisfied with a low-price course. On the Internet, you will get only what you pay for. And this is your future career in the subject area of your dreams. Surely a little initial investment will go a long way in the long run.
Additionally, it will help to gain the employers’ perspective as well. You can refer to this Hiring Guide by TopTal for further reading.
Always keep learning. ML and AI are fields that move forward at an incredible pace. Subscribing to RSS feeds and online websites that keep you updated with the latest developments in the field is something that you absolutely have to do. Nothing shows your commitment to excellence a much as keeping up with the latest state-of-the-art research. And you can do it quite easily by using Reader applications like Feedly and Inoreader. Learning might be something you do in college. But mastery is something you aim towards for your entire lifetime. Never give up. All the best for your job search, which will definitely be successful if you can follow the instructions mentioned here on this blog post. Finally, pay special attention to your portfolio of data science projects on GitHub to make sure you stand out from the competition.
More organizations are adopting data-driven and technology-focused approaches to business and hence the need for analytics expertise continues to grow. As a result, career opportunities in analytics are around every corner. Due to this identifying analytics talent has become a priority for companies in nearly every industry, from healthcare, finance, and telecommunications to retail, energy, and sports.
In this blog, we will be talking about different career paths and option in the Business Analytics field. Furthermore, we will be discussing the qualifications required for being a business analyst and what are the primary roles a Business Analyst handles at a firm
In this blog, we will be discussing
Business analysts, also known as management analysts, work for all kinds of businesses, nonprofit organizations, and government agencies. Certainly, job functions can vary depending on the position, the work of business analysts involves studying business processes and operating procedures in search of ways to improve an organization’s operational efficiency and achieve better performance. Simply put, a Business Analyst is someone who works with people within an organization to understand their business problems and needs and then to interpret, translate and document those business needs in terms of specific business requirements for solution providers to implement.
Most entry-level business analyst positions require at least a bachelor’s degree. Therefore beginning Business Analysts need to have either a strong business background or extensive IT knowledge. Likewise, you can start to work as a business analyst with job responsibilities that include collecting, analyzing, communicating and documenting requirements, user-testing and so on. Entry-level jobs may include industry/domain expert, developer, and/or quality assurance.
With sufficient experience and good performance, a young professional can move into a junior business analyst position. In contrast, some choose instead to return to school to get master’s degrees before beginning work as business analysts in large organizations or consultancies.
Professional business analysts play a critical role in a company’s productivity, efficiency, and profitability. Hence, essential skill sets range from communication and interpersonal skills to problem-solving and critical thinking. Let us discuss each in a bit more detail
First of all, Business analysts spend a significant amount of time interacting with clients, users, management, and developers. Therefore, being an effective communicator is key. You will be expected to facilitate work meetings, ask the right questions, and actively listen to your colleagues to take in new information and build relationships.
Every project you work on is, at its core, is around developing a solution to a problem. Business analysts work to build a shared understanding of problems, outline the parameters of the project, and determine potential solutions. Hence, problem-solving skill is a must-have for this job position.
A business analyst is an intermediary between a variety of people with various types of personalities: clients, developers, users, management, and IT. Therefore, you have to be able to achieve a profitable outcome for your company while finding a solution for the client that makes them happy. This balancing act demands the ability to influence a mutual solution and maintaining professional relationships.
Business analysts must assess multiple choices before leading the team toward a solution. Effectively doing so requires a critical review of data, documentation, user input surveys, and workflow. They ask probing questions until every issue is evaluated in its entirety to determine the best conflict resolution. Therefore, critical thinking skill is a must have pre-requisite for this job position.
A career path of a business analyst usually begins with working at an entry level, and gradually with experience and with acquiring a better understanding of how businesses function, growing up the ladder.
Also, Business Analysts enjoy a seamless transition to different roles according to one’s interest because the profession consists of a set of skills which are highly specialized and can be applied to any industry and to any subject matter area successfully. As a result, this allows for the Business Analyst to move between industry, company and subject matter area with ease which becomes their career progression and a focus of professional development.
Other roles that one can take up after gaining experience as a Business Analyst can be
Once you have several years of experience in the industry, you will finally reach a pivotal turning point where you can choose the next step in your business analyst career. After three to five years, you can be positioned to move up into roles such as IT business analyst, senior/lead business analyst or product manager.
But broadly beyond all the fancy names given to designations, we can consider four levels of professional analytics roles:
Level 1: The Business Analyst
Level 2: The Data Scientist
Level 3: The Analytics Decision Maker
Level 4: The Analytics Leader
Modern Analyst identifies several characteristics that make up the role of a business analyst as follows:
The average salary of a business analyst in India is around 6.5 L.P.A. As one continues to gain the experience in this field, the salary gets more lucrative.
The more experience you have as a business analyst, the more likely you are to be assigned larger and/or more complex projects. After eight to 10 years in various business analysis positions, you can advance to chief technology officer or work as a consultant. You can take the business analyst career path as far as you would like, progressing through management levels as far as your expertise, talents, and desires take you.
So with so many interesting, promising and rewarding options available for Business Analysts, they need to first get a firm hold about the basics of data analysis. You can also have a look at this post to know more about what are the different components in data science. It will help you to boost your business analyst career.
We, at Dimensionless Technologies, offer data science course which helps to make you industry ready. Do go through our website and let us know how we may help you.
The Harvard Business Review called the data scientist ‘the sexiest job of the 21st century’. As problem solvers and analysts, data scientists are the professionals identifying patterns, noticing trends and making new discoveries, often working with real-time data, machine learning, and AI.
Data scientists are in high demand, with forecasts from IBM suggesting that the number of data scientists will reach 28 percent by 2020. In the US alone, the number of roles for all US data professionals will reach 2.7 million. Also, powerful software programmes have given us access to deeper analytics than ever before. This analysis of data generated by people, places and things is a goldmine of invaluable insight
With the increase in demand, many people are rushing into the data science track. With the large influx of people into this field and most of them being freshers, people are committing many basic mistakes while progressing in their data science career.
In this blog, we will be looking at some of the very common mistakes that all of us as data scientists make. We will also try to address these issues with probable solutions to them.
2. Directly jumping to Machine Learning and (fancy) algorithms
Let us all clear this misconception first that machine learning is not everything data science has to offer. Data science is all about solving a given problem. It is a process which starts with understanding the problem and collecting data for the same to delivering insights and solutions for the problem. Between this, machine learning is a small portion(borrowed from computer science field) which helps in making predictions or wiser judgments with the data at hand. Many people directly jump to machine learning or give a lot of importance to it but this should not be the case. It is still ok if you want to be a machine learning engineer in the future but definitely not ok for a data scientist. Machine learning is not everything which data science has to offer. There are statistics, domain knowledge and communication skills attached to it too.
Solution –
The solution here is pretty simple. First, if you are really interested in machine learning, you should focus on its internal math also. You should first ensure you have a good grasp of linear algebra and calculus before directly deep diving into the machine learning. Secondly, one should pay attention to other aspects of data science and should focus on problem understanding more than applying a fancy algorithm to solve it.
3. Considering model accuracy to be supreme
Accuracy isn’t always what the business is after. Sure a model that predicts employee retention probability with 95% accuracy is good, but if you can’t explain how the model got there, which features led it there, and what your thinking was when building the model, your client will reject it. Accuracy sue is important but interpretability holds more importance. Maybe this is the case why deep neural networks are rarely used in the production given they are not highly interpretable.
Solution –
You can have a trade-off between accuracy and interpretability of the model. Try to understand how much accuracy fits in the domain of the business problem and whether the client is interested more in results or understanding of the problem and factors related to it
4. More attention to tools rather than the problem at hand
In data science, tools are not important but the solution to the problem is. It does not matter how you get to the solution considering tools in hand. Tools are for the purpose of making life easier and enabling one to perform tasks quickly hence one should not pay large attention to the usage of tools. For example, one should not try to fit in machine learning everywhere uselessly. Having a solid knowledge of tools and libraries is excellent, but it will only take you so far. Combining that knowledge with the business problem posed by the domain is where a true data scientist steps in. You should be aware of at least the basic challenges in the industry you are interested in (or are applying to).
Solution –
Search for datasets in a specific industry and try to work on them. This will create an impact on your resume. You should also focus on having domain knowledge of the problem you are trying to solve
5. Trying to learn everything at once
This is one of the most common mistakes many data scientists end up doing. Being a jack of all trades and master of none may give your knowledge a lot of breadths and but you will always lack the required depth. You will be able to start an approach to provide a solution to the problem but it will be very rare that you will till the end of it properly. One can not learn everything in one go.
Solution-
Try to find an area of deeper interest within data science and try getting depth in your knowledge and after that, you can work on increasing the breadth
6. Jumping to conclusions without proper validation
I have seen data scientists jumping straight to conclusions without validating results they are getting from their analysis or model predictions.
Solution-
Perform hypothesis testing and validate/invalidate all the insights you have generated by conducting statistical tests for their significance
7. Negligence towards data cleansing, EDA and visualizations
Many data scientist skim over the concepts of data cleaning, EDA and visualizations and move to data modeling. Understanding data first and make it usable for modeling is paramount hence a lot of attention should be given to these topics to emerge out as a successful data scientist
Solution-
Take up datasets from different sources and try finding insights out of them. Try to build a story around datasets with help of graphs and numbers extracted out of the dataset. This practice will help you in understanding the data better
8. Thinking that communication skill is not required
Communications skills are one of the most under-rated and least talked about aspects a data scientist absolutely MUST possess. I am yet to come across a course that places a solid emphasis on this. You can learn all the latest techniques, master multiple tools and make the best graphs, but if you cannot explain your analysis to your client, you will fail as a data scientist. And not just clients, you will also be working with team members who are not well versed with data science — IT, HR, finance, operations, etc. You can be sure that the interviewer will be monitoring this aspect throughout.
Solution-
One of the things, I find most helpful, is explaining data science terms to a non-technical person. It helps me gauge how well I have articulated the problem. If you’re working in a small to medium-sized company, find a person in the marketing or sales department and do this exercise with them. It will help you immensely in the long term.
9. Giving too much importance to coding skills
If you are a data scientist, you will have to code but this is not the hardest part of it. People tend to think that data science is all about coding and should put a lot of attention in coding skills. No doubt coding skills are required but one need not master it all-together.
Solution-
People should focus more on creative ways of solving a problem rather than focussing too much on their coding skills. There are many software’s to perform the tasks for data scientists. Again coding is an essential skill but not a mandatory skill.
10. Insufficient research on the problem at hand
Many problems do not reach a convincing solution just because the initial research on the problem was less or the domain knowledge related to that problem was not sufficient. People tend to jump into the problem directly without getting enough domain knowledge or performing a good initial research on what the problem is and how one should go about it
Solution-
Conduct an extensive initial research and try to get the complete idea of the industry domain of the problem you are dealing with. Try to talk to the people from the same domain and understand how the process flows in that business line.
These mistakes are not easy to avoid when you are starting fresh in the data science career. I have also done many of the above mistakes that I have mentioned above. It takes an experience to understand why all the above points make sense. As we grow in experience, we learn and we get better and emerge out as a champion!
In this world of advanced and futuristic technology which are mostly data driven, Data Scientists are the most sought after people to find solutions to data problems across various industries, ranging from tech to healthcare to government agencies. Depending on the needs of the industry, the requirements of a data science job can vary from cleaning and visualize data to training an AI chatbot. However, there are a few important skills which a Data Scientist should adhere to while applying for a job in any industry. Here are the 5 must have skills for a data scientist –
Mathematics and statistics are the foundation of data science and sound knowledge of both helps a Data Scientist to understand how to deal with a dataset. Knowledge of calculus, linear algebra, descriptive and inferential statistics is required to evaluate the data properly and decipher the relevant parameters to come up with a data-oriented solution.
Programming is a fundamental skill that a Data Scientist should have as it helps to augment statistical methods, analyze and visualize datasets and also to create automation tools to deal with redundant tasks. Knowledge of R and Python programming are important as the former is required mostly for statistical analyses and the latter to work with development of tools based on the performed analyses. Companies working on software development are more interested in Python as it helps to create APIs or deploy code on the server based on the tools created for analyses or automation.
Industry knowledge and product intuition give the ability to understand the complex system which generates all the data. Product knowledge helps a Data Scientist to create hypotheses of different ways a system can behave and produce results. Also, the need for defining metrics of performance of a product and debugging analyses helps a company to keep track of the progress along with various hindrances faced while developing a product.
Effective communication is the key to success and it holds true across all domains or job roles. One of the biggest challenges of being Data Scientist is to explain the analyses to people who are handling the business and a better storytelling method provides the decision makers with a clear and concise way to effectively act on the insights of the analyses. Data visualization is another fundamental skill to learn as a good graph is always better than a bunch of text and numbers.
Being a good Data Scientist also means to use the power of creativity while dealing with data. Creative thinking helps to spot trends, find connections between datasets, cost and time effective ways to perform analyses or produce results, and communicating the results of the analyses in an informative and attractive manner.
For each type of analysis think about:
