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The ISBF Blog

Careers in Data Science

Build Careers in Data Science

Posted on January 08, 2019 by Songbedna Bauri  | Academics | Blog

Many would argue that data science is a relatively new field but the need to identify trends and find insights in data has been existing since ancient times. Wondering when? Well, history states that the Egyptians used census data to boost tax collection and forecast the flooding of the Nile river. So, now you know that although data (structured and unstructured both) mining to identify patterns existed in the past but it was practiced only by a few.

What is data science?

Formerly known as datalogy, data science refers to the theories, collective processes, concepts, technologies and tools that help to analysis, review and extract key information from raw data. Since data science merges varied tools, this multidisciplinary blend uses algorithms and machine learning principles to decode hidden, complex patterns from the raw data.

In today’s business terms, data science is all about using the raw data to make better decisions and generate business value.

Every organization, irrespective of its shape and size, want to curtail costs, improve efficiencies, identify market trends and improve the competitive advantage. In such a scenario, data mining becomes the only way out. This is also one of the key reasons why data science as a field is evolving at a rapid pace. In simple words, the upswing witnessed in data mining and collection is fueling the growth of this industry.

Realizing that data science has the ability to impact the hiring process in organizations, IBM in a report stated the demand for qualified data scientists and advanced analysts is booming and expected to register 28% jump by 2020. A similar opinion was reflected in LinkedIn’s claim that data science is the second fastest growing profession globally and Harvard University labeling of the data scientist’s profession as the sexiest job of the 21st century.

Keeping in mind that every business is keen to evolve their data strategy and the demand for qualified data scientists will grow exponentially, let’s delve deep into the varied nuances of data science – like what it is all about, reasons for being an attractive career choice, benefits of pursuing study in this domain, what are the top job roles aspiring data science majors can explore and so on.

Why do we need data science?

In the traditional systems, data which businesses usually dealt with could be analyzed by using simple business intelligence (BI) tools because it was small in size and mostly structured. But things changed with the emergence of Big Data. While the need for storage grew (which was later solved by Hadoop and other frameworks), data processing became a challenge because it is mostly semi-structured or unstructured.

Reports suggest that over 80% of organizational data (generated from sources such as text files, financial logs, multimedia forms, instruments and sensors) is unstructured. Since BI tools are incapable of processing this humungous amount of data, the need for advanced and sophisticated analytical tools and algorithms for processing, analyzing and deriving result-oriented insights is only possible with Data Science.

Why do businesses use data science?

The popularity of Data Science is growing and expected to scale up further in the coming years as well. Here are some of the reasons why businesses use data science in their day to day affairs-

  • Increase sales: Every online store is keen to sell more. So, identifying the exact needs of clients based on the available data (such as past browsing history, buying trends, age, income, etc) becomes important. Data mining ensures that this goal is attained and make accurate recommendations based on past purchasing history of a customer.
  • Better decisions: Wondering what if your car was intelligent enough to drive you home? With data science, it is possible. The self-driven cars are proficient to collect live data from sensors (such as cameras, lasers and radars) to develop a map of its surroundings. Using the available advanced machine learning algorithms, it decides when to increase speed, when to slow down, whether to overtake, etc.
  • Predictive analysis: Let’s take weather forecasting as an example. Predicting natural calamities has always been an extremely challenging task. With data science, it is possible to garner weather information about a particular region, find the strength and direction of tropical storms, identify areas where flooding is likely to be most severe and so on.

Why is data science such a hot career right now?

According to Glassdoor’s 50 Best Jobs in America For 2018, data scientist has been named as the best job in the US. Reason? The demand for data science majors is high and the supply is low. Let’s take a close look to understand the nitty-gritty of demand and supply –

  • Demand:

    Earlier, analysts used excel to analyze data and academics choose SPSS, Stata, but nowadays data-based decision making is preferred. According to PwC, 39% of companies are in favor of data-driven decision making.

    Today’s businesses prefer to use advanced technology and tools such as Google Analytics (for marketing), SAP and Microsoft Analytics (for business analysts), Sisense and Tableau (for BI teams) to improve results and efficiency.

    Global giants like Google, Facebook, Amazon are data dependent to boost their presence, maintain competitiveness and stay relevant in today’s times.

  • Supply:

    The success of data science is dependent on technology and hence, it wasn’t possible for it to exist two decades back because of the use of primitive programming languages, slow computers and so on. Also, there were hardly any data science courses/programmes in the past. In fact, the situation hasn’t changed much. Only a handful have the knowledge/expertise to work in this industry, which is why the salaries continue to remain high.

    Keeping the supply-chain dynamics in mind, pursuing a career in data science is a good choice at the moment.

Benefits of having a career in data science

Keeping in mind that data science as a career option is becoming hugely popular among students who are planning Master’s in Data Science, here are few of the advantages of pursuing a course after class 12 in this field –

  • Explore opportunities in any sector:

    Data science allows job seekers to work in any industry of their choice. The flexibility is possible in data science only because the core technology is the same behind all sectors. So, you can work with an eCommerce player, auto manufacturer or take up independent projects of your choice.

  • Work with MNCs:

    After pursuing Master’s in Data science, your chances of landing a job with an MNC is much higher. Reason? Organizations like Apple, Uber, Amazon etc rely on data science heavily for their day to day operations. For instance, the recommendation displayed in Amazon uses big data. Apple also relies on big data to decide on product features and the surge pricing feature in Uber works on data science.

  • Attractive pay package:

    Glassdoor has stated that $110,000 is the average base salary of Data Scientists in the US. Since 2011, more than 90% of the US graduates who took up data scientist jobs were paid an average salary of $114,000.

  • Global education:

    There are Universities and colleges that offer courses in data science in India and abroad. It is suggested that when you are pursuing post graduate data science programme, look for a course module that follows a global curriculum. It is best to choose a Master’s degree that offers in-depth knowledge and helps you understand the techniques and subject matter based on theoretical statistics and machine learning to analyse unknown datasets, perform independent data analysis and use statistical software to analyse datasets.

    Since the world will generate 50 times more data in 2020 compared to 2011, certified courses improve the chance for higher paycheck, promotion and resume shortlisting than the self-taught ones.

  • Always in demand:

    A student who has pursued a career in data science is free to explore any path – consultancy, project management, security, system architecture etc. In these areas of specialities, the demand for well-qualified professionals is always on the rise.

Build Careers in Data Science

Tips for People starting a Career in Data Science/ How to start a career in data science

Preparing for any career requires a lot of hard work and data science is no different. The McKinsey Global Institute projects that the US will have about 250,000 open data science jobs by 2024. By all accounts, the demands in data science are increasing speedily and as an industry, it stands as a lucrative proposition for any aspiring engineers. The popularity of data science as a career choice can be judged from the glut of courses in machine learning, artificial intelligence, data analytics and so on.

While it is interesting to watch the growing demand for data science, but there is a lack of clarity among many who want to start a career in data science. These below mentioned useful tips will come in handy to those who are eager to start a career or on the threshold of launching their career in data science.

  • Select the ‘right’ role:

    In the data science domain, there are different kind of roles such as machine learning expert, data science expert, data visualization expert, data engineer to a name a few. It is best to choose a job profile keeping the course module in mind. If you are unsure about the role, you can take mentorship from people who are working in this domain and understand the job requirements.

    After you have chosen the role, it is best to understand what it entails. Until and unless you understand the difference between a Data Scientist, Data Analyst and other related profiles, you may end up choosing the wrong one.

    Data mining, data analytics, machine learning are part of data science. Although data science is an umbrella term, the role of a data scientist is different from a data analyst. While a data scientist projects the future based on past patterns, a data analyst is responsible for extracting insights from different data sources.

  • Choose a tool/language:

    It is best to start with the language you are familiar with. If coding is not your forte, go for GUI-based tools. Once you have become well-versed with the concepts, then go for coding.

    Both python and R are excellent choices as programming languages. While academia prefers R, Python is preferred in the industry. Both the languages have its own set of strengths. To get started, you don’t need to master both R and Python. Instead, the focus should be on learning any one of the languages and its ecosystem.

  • Master data analysis, manipulation & visualization with pandas:

    When working with data in Python, ensure that you learn to use the pandas library. Pandas are important because it provides “DataFrame” (high-performance data structure), suitable for tabular data with columns of varied types, resembling an Excel spreadsheet or SQL table. Also, it comprises tools for reading and writing data, handling lost data, filtering and cleaning disorganized data, integrating datasets, visualizing data, etc.

    In layman words, learning pandas assume relevance because it strengthens the learner’s efficiency when dealing with data.

  • Study Machine learning:

    When studying machine learning, ensure that you find an answer to some of these questions –

    • Is it possible to find out which machine learning model is most suitable for my dataset?
    • Is it possible to interpret the results of my model?
    • Is it possible to judge if my model will generalize to future data?
    • Which are the features that I should consider including in my model?
  • Don’t ignore practical applications:

    Practical applications are key because it will help you understand if the theory works in reality or not. When you are pursuing a Master’s in Data Science, ensure that you work on open data sets and take a look at the solutions by people who are/have worked in the data science field.

  • Join a peer group:

    Many of you may question why a peer group? It is the best way to stay motivated and updated about the latest trends. It will also allow you to share the experience with those who have similar goals like you.

  • Improve your communication skills:

    It is a common misconception amongst people that if you are technically sound in the data science field, you can bag your dream job. This is a ‘myth’. The job of a data scientist is not limited to crunching data only. One of their important job responsibilities is to communicate customer analytics in a clear and accessible language. So, make communication your forte so that you can share your ideas and convince effectively.

Many aspiring data scientists are of the belief that if you teach yourself how to use a specialized tool (like pandas, scikit-learn, R etc), you can excel in this field. Unfortunately, that is not the case. These specialized tools are just a small part of this wide domain and a thorough knowledge of statistics is an absolute must to ensure success in the data science field. The only way to make a mark in data science is by identifying a question that can be answered with extant datasets.

The Data Science Industry: Who Does What

Here is a detailed list of the variety of different roles and jobs that the aspiring data science enthusiasts can choose from –

  • Data Analyst:

    They are responsible for key tasks like munging, visualization, processing data and perform queries on the databases. Optimization is the most important skill of a data analyst because they will be responsible for creating and altering algorithms without corrupting the data.

    To become a data analyst, the technologies you are expected to know are SQL, R, SAS, Python apart from impeccable problem-solving qualities.

  • Data Engineers:

    They are the ones who build and test scalable Big Data ecosystems so that data scientists can run their algorithms on the stable and optimized data systems. They are the ones who update the existing systems with upgraded versions of current technologies so boost the efficiency of the databases.

    Hive, NoSQL, R, Ruby, Java, C++, and Matlab are the technologies you should master when building a career as a data engineer. Additionally, if you are proficient to work with popular data APIs and ETL tools, it will be an added advantage.

  • Database Administrator:

    They ensure proper functioning of all the databases in an organization and handle database backups and recoveries. Also, they grant or withdraw its services to the employees based on their requirements.

    A database administrator is expected to be proficient in data security, data modeling and design.

  • Machine Learning Engineer:

    They should be well versed in technologies such as SQL, REST APIs. Also, they should know how to perform A/B testing, develop data pipelines and implement common machine learning algorithms like classification, clustering, etc.

    Java, Python, JS should be a machine learning engineer’s forte apart from having a robust understanding of statistics and mathematics.

  • Data Scientist:

    They should identify the challenges of a business and provide solutions using data processing and data analysis. Data Scientists are the ones who perform predictive analysis, initiate a fine-toothed comb via disorganized data to offer actionable insights.

    An expert in R, MatLab, SQL, Python is best suited for the job of a Data Scientist. You can try your hand in this role if you have a higher degree in mathematics or computer engineering.

  • Data Architect:

    They are responsible for creating the blueprints for data management to ensure databases can be integrated, centralized and protected. They offer data engineers with the best tools and systems to maximize productivity and best results.

    When building a career in data architecture, expertise in data warehousing, data modeling, extraction transformation and loan (ETL) are a must. Also, a strong grasp on Hive, Pig, and Spark comes as an added advantage.

  • Statistician:

    They are well versed with statistical theories and data organization. Apart from extracting and providing insights from the data clusters, they create new methodologies that engineers can implement.

    A statistician should be good with different database systems such as SQL, data mining, and machine learning technologies.

  • Business Analyst:

    A business analyst is expected to have a sound knowledge about the working of data-oriented technologies and understand the management of large volume of data volumes. They should know how big data can be used for driving business growth. They act as a link between the management executives and the data engineers.

    Hence, they should have knowledge of business finances, business intelligence, and IT technologies (like data visualization tools and data modeling).

  • Data and Analytics Manager:

    They are the ones who supervise the data science operations and allocates work to their team based on skills and expertise. They should be proficient in technologies like SAS, R, SQL.

Conclusion

One thing is clear, data science is flourishing and growing rapidly. The demand for data science majors is not just within in the tech companies, a lot of financial services enterprises are keen to hire them as well. Although the analytics bubble is not likely to burst anytime in the future, the skills can still be transferred to other roles across industries.

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