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    Categories: Data Analytics Certification

How to Build a Successful Career in Data Science?

Data science is an amalgamation of data analytics, software engineering, data engineering, machine learning, analysis, business analytics and more. It includes Big Data which comprises retrieval, collection, ingestion, and transformation of large amounts of data. Data science is responsible for giving form to big data, analyzing patterns and advising decision-makers to bring in the changes that effectively add to business growth. If you are looking at data science as a career be prepared to deal with large volumes of structured and unstructured data.

Build a Successful Career in Data Science

Required Skills

To be a successful data scientist, knowledge of machine learning, understanding multiple analytical functions, hands-on experience in SQL database coding and a strong knowledge of programming languages and tools like Python, SAS, R, PIG, HIVE, Scala etc. are a must. A data scientist would also be required to use a distributed computing framework like HADOOP and data storytelling.

The next question would be, how exactly does one build a successful career in data science?

Selection of Correct Data Roles

Data analysis, analytical management, data mining, machine learning, data architecture are some options available under the data science umbrella. A jack of all trades is most unsuitable for data science. So, the first step would be to choose your area of interest and then work towards your goal. Do a SWOT analysis on yourself. It will help to make an objective selection.

Familiarize Yourself with Tools/Languages

It would be advisable to get comfortable with tools like Pandas, sci-kit-learn, matplotlib, etc. You would also be required to familiarize yourself with programming languages like Python and R.A data scientist is a statistician and programmer rolled into one.

Polish Your Communication Skills

A data scientist has to explain the complexities of data analysis in the simplest form to business associates or even within the organization. This requires special communication skills.

Connect with Peers

Working with peers not only increases knowledge it also widens your perspective. Close connections with peers will encourage the sharing of ideas. Solving problems with real-time data can become easier when you work in groups.

You could also make use of forums like Quora, Reddit, Stack Overflow for support in case of a hurdle.

Educational Requirements

Formal education is imperative to become a data scientist. There are many courses in data science available at various accredited institutions that can help achieve your goals. However, it would be wise to ascertain if the course covers the basics of your data role before applying.

A data science course comprises several modules like Python, R, Linear Algebra, Statistics, Machine Learning Algorithms, etc. The best fulltime data science courses are offered by IIT (Delhi), IIT (Kharagpur), IIM (Bangalore), IIT (Bangalore), Great Lakes Institute of Management etc.

For working professionals, online data science courses are advised. The best online executive certificate programs in Data Science are offered by Talentedge in partnership with premier institutes like XLRI Jamshedpur, IIM Kozhikode, SPJIMR etc. The Professional Program for Data Science includes technologies like T-SQL, Microsoft Excel, Power BI, Python, R, Azure Machine Learning, HDInsight, Spark.

The Most Popular Career

Data science as a career is very rewarding in terms of salary and job satisfaction. If you have decided to make a successful career in data science, take proper steps in that direction as outlined above.

More Information:

What does a career in Data Analytics look like?

How is Big Data Analytics Different from Data Science?

6 reasons for you to opt for big data analytics certification

Kickstart your Career in Analytics with Data Science Program

Machine Learning Vs Big Data Analytics: Which Is A Better Career Option?

Executive Post Graduate Programme In Data Science from IIIT BANGALORE

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