Data science has rapidly become a popular career option over the years. With the growing possibilities of artificial intelligence and other areas, data science has come to be a relevant field of research and work.
If you too are interested in data science as a career, then this blog will help you provide information about its potential. Let us begin with the basics of data science and move on to other essential questions.
What is data science?
Data science is a multidisciplinary domain which makes use of statistics, algorithms, data analysis and machine learning techniques to study patterns and other phenomena. The insights gained from that are then used for problem-solving and further research in several different scientific and technical areas.
What does a data scientist do?
Data scientists study given data to discern trends or characteristics. They employ a number of relevant techniques and research models to make sense of the data. The conclusions they come to can then be used to provide businesses and agencies with important insights. These results can then be used by the businesses to strategize and design better policies or products.
Besides this, data scientists can also help in developing the tools used in data science. They play key roles in creating algorithms, testing, and research, and building other tools.
|Data Scientist||Studying and analyzing data, Using and building research tools and algorithms, Providing companies or businesses with useful insights based on data|
Why build a career in data science?
All sorts of businesses today invest in data science and analysis to make better decisions for both themselves and their customers. Data scientists have become an asset to most companies and teams.
Scope of data science
• In India, major sectors like healthcare, pharmaceuticals, banking, telecommunications, e-commerce, and media require data scientists.
• India is second only to the US when it comes to data science jobs. 1 out of every 10 data science or analytics jobs is accounted for by India. (Quartz India)
Demand for data science
• Currently, the biggest employer of data scientists is the banking and finance sector, comprising about 44% of total data science jobs. By 2020, India will create 39,000 more data science jobs spanning sectors like agriculture and aviation.(Business Today)
• Evolving technologies mean that data science will see a big demand in fields like AI, cyber security, space exploration and driverless transportation too. (The Economic Times)
Pros and cons of a career in data science
|Plenty of job openings||Requires good knowledge of multiple disciplines and tools|
|High demand in major sectors||Low possibilities for independent research work|
|Creative application of mathematical and statistical skills||The use of data analysis results are controlled by the sector or company’s needs and demands|
Qualification required for data science
Since data science uses a multidisciplinary approach to problem solving and analysis, you need a strong background in mathematics, statistics, and computer science before you can become a data scientist. Preferences are given to candidates with a good knowledge of programming languages and relevant work experience too.
|Degree||Field of Study|
|Bachelors||Maths, Statistics, Computer Science, IT, Engineering, Physics|
|Masters||Data Science, Applied Maths, or related fields|
A significant percentage of data scientists also earn a PhD in their fields. It is important to learn programming skills as part of your university or as add-on skills. Online courses and certifications, internships and work experience also go a long way in a data science career.
Data science jobs in India
Let us look at the vacancies for data science and related jobs in India.
Note: The data provided above was last updated in March 2019. It is subject to change.
Job roles in data science
|Data Scientist||Analyzing raw data, using data analysis techniques and tools, sharing insights with companies, strategizing|
|Data Analyst||Processing data sets, visualization, optimization, creating algorithms, performing queries on databases|
|Data Engineer||Using Big Data technology and Hadoop, creating useful software, working with SQL technologies, providing data warehousing solutions|
|Business Intelligence Professional||Identifying how Big Data can be used, interpreting high volumes of data, providing relevant insights for business solutions|
|Statistician||Using statistical tools, organizing data, extracting information from data sets, creating statistical theories and methodologies|
|Machine Learning Engineer||Carry out A/B testing, building and implementing algorithms and data pipelines, producing data-based products or services, helping with operations,|
Data science salaries
Average data science job salaries can vary according to skills and experience. Here is an average annual salary data for data science and related job roles.
|Job Role||Average Annual Salary (0-3 Years of Experience)||Salary Range (LPA)|
|Data Scientist||6.3 lakhs||3 to 20|
|Data Analyst||4.9 lakhs||1.9 to 8.2|
|Data Engineer||5 lakhs||3.4 to 20|
|Business Analyst||5.8 lakhs||2.5 to 10|
|Machine Learning Engineer||7 lakhs||3.2 to 20|
|Statistical Analyst||5.8 lakhs||1.9 to 10|
Data source: AmbitionBox
Data science skills
Take a look at the skills necessary to work in data science.
|Mathematics||Strong understanding of multivariable calculus and linear algebra|
|Statistics||Knowledge of tools and techniques to find out patterns and co-relations in data|
|Programming and languages||Ability to use programs like R, Python, SQL and Hadoop|
|Data wrangling||Dealing with imperfect or inconsistent data and unstructured data for analysis and extracting useful information|
|Data visualization||Using visualization tools to present the information found from data analysis and communicating them to the company|
|Machine learning||Working knowledge of algorithms and other facets like neural networks and adversarial learning|
- Soft skills:
Curiosity, creativity, communication, critical thinking, business sense, and teamwork
|Data Scientist||Apache Graph, Hadoop, Apache Pig, Apache Storm, D3.js, Network X, GNU Octave, Rapid Miner, etc.|
|Data Analyst||Spark, Excel, KNIME , pandas, Spot fire, Bokeh, etc.|
|Data Engineer||Hive, Mesos, HBase, Cascading, R Studio, Scala, etc.|
|Machine Learning Engineer||Scikit-learn, BigML, Data Robot, GraphLab Create, Logical Glue, ML Base, Tensor Flow, etc.|
Common career paths to become a data scientist
Here are some common ways to become a data scientist.
Career Path 1:
Earn a Bachelors degree in Computer Science → Get certification in Big Data/Data Analytics → Join as a Data Scientist or Engineer intern or employee at a firm
Career Path 2:
Earn a Bachelors and/or Master’s degree in Applied Mathematics or Statistics → Complete online courses in Programming languages and Data Science/analytics → Complete projects on platforms like Kaggle and build a portfolio → Apply for jobs in Data science
Career Path 3:
Earn a Bachelors degree in Physics → Take online courses in Programming languages→ Collect professional certifications in Data Science and Machine Learning → Intern or get a job as a Data Scientist or Machine Learning Engineer
Career Path 4:
Earn a Bachelors degree in Business Administration → Opt for a Master’s degree in Data Science or Marketing/Business Analytics → Intern or find a job as a Data Analyst
Note: The above career paths are just examples of common career paths. There is no fixed career path to start a career in data science. The career steps can vary as per the background, interests, and skills of the individual.
Tips to get a job in data science
These are some essential skills you must have to make a career in data science.
1. Build strong mathematical and statistical skills
A solid base in applied math and statistics can be very helpful in data science. It is a key skill for analysing the large volumes of data and data trends collected by big companies.
2. Learn programming languages
Languages like Python and R are very relevant for getting a head-start in a data science career. Knowing how to code is also essential for working with big data.
3. Courses and certifications
There are a whole variety of pertinent and interesting data science courses and programs, both online and offline. These programs include data mining, statistical tools, coding, machine learning, etc. A couple of such certifications in your pocket will help you increase your employability chances as a skilled data scientist.
4. Work on data science projects
Learning how to use tools and techniques are not the only way to become a data scientist. Building a portfolio by working on real projects, either as an intern in a relevant company or independently on platforms like Kaggle can be very beneficial.
5. Develop business acumen
Companies want to hire data scientists for the valuable inputs they can provide during the process of strategizing and product-building. Knowing how to use the results of data analysis and present them as creative insights or business solutions is a brilliant way to sharpen your credibility as a data scientist.
Find and apply for Data Science jobs.
Data science blogs
Here is a list of popular data science blogs:
- Data Science Central
- NYC Data Science Academy
- Data Camp
- Inside Big Data
- No Free Hunch
- Smart Data Collective
- KD Nuggets
- Towards Data Science
Data science books
Here is a list of popular data science books:
- What is Data Science by Mike Loukids
- The Master Algorithm by Pedro Domingos
- Business Analytics: The Science of Data-driven Decision Making by U Dinesh Kumar
- Data Structures and Algorithms Made Easy by Narasimha Karumanchi
- Marketing Data Science by Thomas W. Miller
- Big Data Demystified by David Stephenson
- Machine Learning by Tom M. Mitchell
- Python Machine Learning By Example by Yuxi (Hayden) Liu
- R for Data Science by Hadley Wickham
- Data Structure and Algorithmic Thinking with Python by Narasimha Karumanchi
Data science YouTube videos and channels
Here is a list of popular data science videos and channels on Youtube:
- Data science tutorial for beginners by Edureka
- Introduction to date science by Simplilearn
- StatQuest with Josh Starmer
- Data School
- Siraj Raval
Data science forums
Here is a list of popular data science forums:
- IBM Data Science Community
- Data Science on Reddit
- Data Science Central
- Analytics Vidhya
- Quora – Data Science
- MachineLearning on Reddit
- Kaggle Forum
- Data Science Stack Exchange
- R Nabble
Data science webinars
Here is a list of popular data science webinars:
- R and Tableau
- Data Science Central
Top companies to follow
Here are some companies which are working with big data and machine learning:
From big companies to smaller startups, data science is becoming increasingly handy in building their infrastructure and business strategies. With data science and machine learning or AI growing to be the hottest jobs in recent years, there will only be an increase in opportunities and avenues to follow in the near future.