Company: Syntel Profile: Associate Consultant Project: Reporting and BI Location: India
Current Profile
Company: TCS Profile: Business Analyst Project: Data Science Location: India
My journey into Data Science
Why Data Science?
I like to keep myself updated with the latest trends in the IT industry. Back in 2015, when I started my career, my choices were Data Science or Cloud Computing. After a bit of research about these domains, it was clear that with my analytical and logical skills I could do really well in Data Science.
Why Dimensionless?
One of my project-colleagues enrolled in the Data Science program at Dimensionless and he really liked the course. At first, I was a little sceptical about an online course since I am used to learning in a physical classroom. As my colleague was completing the course, he got transferred to a Data Science project internally!
That was an eye-opening moment for me. I attended a class along with him, all the students were asking doubts and getting it resolved. That helped me make the decision.
Experience with Dimensionless?
Even more comfortable than physical classes. The best part was that I could get my doubts resolved at any time. The teachers were helping and kept the classes very interactive. This always kept me motivated to self-study and practice, study Data Science projects on my own. I also got a lot of help from their alumni groups. Even to this day, I am in touch with my mentors at Dimensionless.
Career Transition to Data Science
Once I was comfortable in the course, the transition felt natural. And after solving case studies under the guidance of Dimensionless I was able to smoothly switch to a Data Science profile within the company itself.
I think if you follow the curriculum and course path along with gaining as much project-related knowledge as possible, you cannot go wrong with Data Science. Keep learning, keep upgrading.
Reports suggest that around 2.5 quintillion bytes of data are generated every single day. As the online usage growth increases at a tremendous rate, there is a need for immediate Data Science professionals who can clean the data, obtain insights from it, visualize it, train model and eventually come up with solutions using Big data for the betterment of the world.
By 2020, experts predict that there will be more than 2.7 million data science and analytics jobs openings. Having a glimpse of the entire Data Science pipeline, it is definitely tiresome for a single human to perform and at the same time excel at all the levels. Hence, Data Science has a plethora of career options that require a spectrum set of skill sets.
Let us explore the top 5 data science career options in 2019 (In no particular order).
1. Data Scientist
Data Scientist is one of the ‘high demand’ job roles. The day to day responsibilities involves the examination of big data. As a result of the analysis of the big data, they also actively perform data cleaning and organize the big data. They are well aware of the machine learning algorithms and understand when to use the appropriate algorithm. During the due course of data analysis and the outcome of machine learning models, patterns are identified in order to solve the business statement.
The reason why this role is so crucial in any organisation is that the company tends to take business decisions with the help of the insights discovered by the Data Scientist to have an edge over the company’s competitors. It is to be noted that the Data Scientist role is inclined more towards the technical domain. As the role demands a wide range of skill set, Data Scientists are one among the highest paid jobs.
Core Skills of a Data Scientist
Communication
Business Awareness
Database and querying
Data warehousing solutions
Data visualization
Machine learning algorithms
2. Business Intelligence Developer
BI Developer is a job role inclined more towards the Non-Technical domain but has a fair share of Technical responsibilities as well (if required) as a part of their day to day responsibilities. BI developers are responsible for creating and implementing business policies as a result of the insights obtained from the Technical team.
Apart from being a policymaker involving the usage of dedicated (or custom) Business Intelligence analytics tools, they will also have a fair share of coding in order to explore the dataset, present the insights of the dataset in a non-verbal manner. They help in bridging the gap between the technical team that works with the deepest technical understanding and the clients that want the results in the most non-technical manner. They are expected to generate reports from the insights and make it ‘less technical’ for others in the organisation. It is noted that the BI Developers have a deep understanding of Business when compared to Data Scientist.
Core Skills of a Business Analytics Developer
Business model analysis
Data warehousing
Design of business workflow
Business Intelligence software integration
3. Machine Learning Engineer
Once the data is clean and ready for analysis, the machine learning engineers work on these big data to train a predictive model that predicts the target variable. These models are used to analyze the trends of the data in the future so that the organisation can take the right business decisions. As the dataset involved in a real-life scenario would involve a lot of dimensions, it is difficult for a human eye to interpret insights from it. This is one of the reasons for training machine learning algorithms as it easily deals with such complex dataset. These engineers carry out a number of tests and analyze the outcomes of the model.
The reason for conducting constant tests on the model using various samples is to test the accuracy of the developed model. Apart from the training models, they also perform exploratory data analysis sometimes in order to understand the dataset completely which will, in turn, help them in training better predictive models.
Core Skills of Machine Learning Engineers
Machine Learning Algorithms
Data Modelling and Evaluation
Software Engineering
4. Data Engineer
The pipeline of any data-oriented company begins with the collection of big data from numerous sources. That’s where the data engineers operate in any given project. These engineers integrate data from various sources and optimize them according to the problem statement. The work usually involves writing queries on big data for easy and smooth accessibility. Their day to day responsibility is to provide a streamlined flow of big data from various distributed systems. Data engineering differs from the other data science careers as in, it is concentrated on the system and hardware that aids the company’s data analysis, rather than the analysis of data itself. They provide the organisation with efficient warehousing methods as well.
Core Skills of Data Engineer
Database Knowledge
Data Warehousing
Machine Learning algorithm
5. Business Analyst
Business Analyst is one of the most essential roles in the Data Science field. These analysts are responsible for understanding the data and it’s related trend post the decision making about a particular product. They store a good amount of data about various domains of the organisation. These data are really important because if any product of the organisation fails, these analysts work on these big data to understand the reason behind the failure of the project. This type of analysis is vital for all the organisations as it makes them understand the loopholes in the company. The analysts not only backtrack the loophole and in turn provide solutions for the same making sure the organisation takes the right decision in the future. At times, the business analyst act as a bridge between the technical team and the rest of the working community.
Core skills of Business Analyst
Business awareness
Communication
Process Modelling
Conclusion
The data science career options mentioned above are in no particular order. In my opinion, every career option in Data Science field works complimentary with one another. In any data-driven organization, regardless of the salary, every career role is important at the respective stages in a project.
Data Industry is on boom today and it seems no shortage of intelligent opinions about the job responsibilities and roles accelerating the data industry. Most of the people are usually confused between the role of a Data Scientist and the Data Analyst. Even if both of them deal with Data only still there are plenty of significant differences that make them suitable for different job positions.
Here, we will discuss how to differentiate Data Scientist from Data Analyst, and their job roles too. Before we switch on the actual topic, let us have a quick look at the differences. Later on, we will try to find out the reasons for the diminishing gap between data scientists and business analysts today. We will try to analyse if there is actually any gap between the two roles and look further into it.
Difference Between a Data Scientist and Business Analyst
A company relies on its business analysts to gain business insights by interpreting and analyzing data and predicting trends-related aspects which help in making critical business decisions. Business analysts also focus on end-to-end automation to eliminate manual intervention and optimizing business process flows which can increase the productivity and turnaround time for an efficient and successful end result. They also recommend systems changes needed to optimize an organization’s overall execution.
Data scientists, on the other hand, specialize and purely rely on data which is further broken down to simpler facts and figures by using tools such as statistical calculations, big data technology, and subject matter expertise. They use data comparison algorithms and methodologies to identify and determine potential competitors or resolve day-to-day business issues.
Business analysts often work on preconceived notions or judgments related to the factors that help drive the businesses. Data scientists, whereas; have had an edge over business analysts, as they leverage data related algorithms which provide accuracy and also use mathematical, statistical, and fact-based predictions.
As organizations are proactively defining new initiatives and campaigns to evaluate the existing strategy on how big data can help to transform their businesses, the role of business analyst is slowly but certainly widening into a major role.
Upgradation in Duties of Business Analysts and Data Scientists
In recent times, there have been a lot of advancements in the data science industry. With these advancements, different businesses are in better shape to extract much more value out of their data. With increased expectation, there is a shift in the roles of both data scientists and business analysts now. The data scientists have moved from statistical focus phase to more of a research phase. But the business analysts are now filling in the gap left by data scientists and are taking their roles up.
We can see it as an upgrade in both the job roles. Business analysts now hold the business angle firm but are also handling the statistical and technical part of the things too. Business analysts are now more into predictive analytics. They have reached a stage now where they can use off-the-shelf algorithms for predictions in their business domains. BA’s are not limited to just reporting and business but now are more into the prescriptive analytics too. They are handling the role of model building, data warehousing and statistical analysing.
Keep a note here that Business analysts are in no way replacing Data scientists. Data scientists are now researching new methods and algorithms which can be used by Business analysts combined with their business acumen in specific business domains.
Recent Advancements in Data Analytics
Data analytics is a field which witnesses a continuous revolution. Since data is becoming increasingly valuable with each passing time, it has been now treated with great care and concern. To cope up with the constant changes in the industries and societies as a whole, new tools, techniques, theories and trends and always introduced in the data analytics sector. In this article, we will go through some of the latest data analytics opportunities which have come up in the industry.
1. Self-service BI
With self-service BI tools, such as Tableau, Qlik Sense, Power BI, and Domo, managers can obtain current business information in graphical form on demand. While a certain amount of setup by IT may be needed at the outset and when adding a data source, most of the work in cleaning data and creating analyses can be done by business analysts, and the analyses can update automatically from the latest data any time they are opened.
Managers can then interact with the analyses graphically to identify issues that need to be addressed. In a BI-generated dashboard or “story” about sales numbers, that might mean drilling down to find underperforming stores, salespeople, and products, or discovering trends in year-over-year same-store comparisons. These discoveries might in turn guide decisions about future stocking levels, product sales and promotions, and even the building of additional stores in under-served areas.
2. Artificial Intelligence and Machine Learning
Artificial intelligence is one such data analytics opportunity which is finding widespread adoption in all businesses and decision-making applications. As per Gartner 2018, as much as 41 per cent of organizations have already adopted AI into some aspect of their functioning already while the rest 59 per cent are striving hard to do the same. There is considerable research going on at present to incorporate artificial intelligence into the field of data science too. With data becoming larger and more complex with each passing minute, management of such data is getting out of manual capacities very soon. Scholars have therefore now turned to the use of AI for storing, handling, manipulating and managing larger chunks of data in a safe environment.
3. R language
Data scientists have a number of option to analyze data using statistical methods. One of the most convenient and powerful methods is to use the free R programming language. R is one of the best ways to create reproducible, high-quality analysis since unlike a spreadsheet, R scripts can be audited and re-run easily. The R language and its package repositories provide a wide range of statistical techniques, data manipulation and plotting, to the point that if a technique exists, it is probably implemented in an R package. R is almost as strong in its support for machine learning, although it may not be the first choice for deep neural networks, which require higher-performance computing than R currently delivers.
R is available as free open source and is embedded into dozens of commercial products, including Microsoft Azure Machine Learning Studio and SQL Server 2016.
4. Big Data
the applications of the Big Data world. Well, most of us are now more than familiar with terms like Hadoop, Spark, NO-SQL, Hive, Cloud etc. We know there are at least 20 NO-SQL databases and a number of other Big Data solutions emerging every month. But which of these technologies see prospects going forward? Which technologies are going to fetch you big benefits?
Why the Role Update?
1. Advancement in technology
There have been a lot of technological advancements in data science. Machine learning, deep learning, automatic data processing are just to name few. With all these new technologies, organisations are expecting more out of their business analysts. Organisations are looking to leverage all these technologies into their decision-making process. To fulfil this, business analysts need to upgrade their role and take the role of data scientists too. Also, data scientists are more towards researching new methods and algorithms. They are the ones now bringing innovation in data science one after another.
2. Identification of more areas of application
Organisations are now able to explore more areas where they can leverage the power of data science. With more applications, organisations are aiming to automate their decision-making process. Business analysts need to step up for more diversified applications. Hence, they have to expand their skillset and takes upgraded roles. Decision scientists are more towards finding newer methods which can help the BA’s in solving complex business problems.
3. Increase in complexity of the business problem
Applications of data science in business are getting both complicated and complex day by day. With an increase in complexity. business analysts have now more prominent and complex roles. This can be one reason where the new BA’s may need to expand their skillset. This is due to the fact that organisations are expecting more out.
4. Growth of data
There has been a tremendous increase in data generation, practices like BIG data are coming as a prominent player in the picture. Business analysts today may need to be handy with Big data technologies rather than just having a business mindset towards the problem.
5. Lack of qualified talent
Today, there is also lack of qualified professionals in data science. This results in one individual taking multiple roles like BA, data engineer, data scientist etc. There are no clear boundaries between these roles in most of the organisations today. So a business analyst today, should also have knowledge of maths and technology. This is one reason too about business analysts acting as data scientists in many organisations.
The Tools of the Trade
The world of a business analyst is business-model centric. Either they are reporting, discussing, or modifying the business model. Not only must they be proficient with Microsoft Office, but they also must be excellent researchers and problem-solvers. Elite communication skills are also a must, as business analysts interact with every facet of the business. They must also be “team players” and able to interact and work with all departments within a company.
Data scientist’s job descriptions are much different than business analysts. They are mathematicians and understand the programming language, as opposed to reporting writers and company communicators. They, therefore, have a different set of tools they use. Utilizing programming languages, understanding the principles of machine learning, and being able to generate and apply mathematical models are critical skills for a data scientist.
The commonality between business analysts and data scientists is that both of them require generating and communicating figure-rich reports. The software used to generate such reports may be the same between the two different positions, but the content of the reports will be substantially different.
Which is Right for You?
If deciding between a future career between a business analyst and a data scientist, envisioning the type of position you want should steer you in the right direction. Do you like interacting with people? Do you like summarizing information to make reports? If so, you are more likely to be happy with a business analyst position than a data scientist. This is because data scientists work more independently. Data scientists are also more technical in nature. So if you have a more technical background, a career as a data scientist might before you.
Summary
In any case, organisations are now on the lookout for new age business analysts. They need to be a combo of the intelligence of knowing the right analytic tools, big data technology, and machine learning. Companies should rather not simply rely on business analysts to predict the future of a business. So if you are a business analyst then you have a lot to learn to stay relevant. But the good news is, there are various data science programs which can help you retool to stay competitive.
Follow this link, if you are looking to learn more about data science online!
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