How can I become a data scientist?

It’s really good that you want to become Data scientist. Most of the people think that it is very difficult to become a Data scientist.

But, let me clear it is not tough, if you will work very smartly in a right direction then you can easily become Data Scientist.

For becoming a Data Scientist, first understand who are Data scientists, then learn what skills are required for Data scientist after that see its roles and responsibilities. In last according to its roles and responsibilities try to add those skills in yourself.

First of all let us know who Data Scientists are?

Data scientists are a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved.

Data scientists are big data wranglers. They take a huge amount of messy data points (unstructured and structured) and clean, massage and organize them with their formidable skills in math, statistics, and programming. Then they apply all their analytic powers to uncover hidden solutions to business challenges and present it to the business.

Data Scientist needs to have both technical and non-technical skills to perform their job in an effective manner.

Technical skills are involved at 3 stages in Data Science. They include:

• Data Capture & pre-processing
• Data Analysis & pattern recognition
• Presentation & visualization

Some job duties of Data Scientists:

• Transforming unruly data into a more usable format.
• Solving business-related problems using data-driven techniques.
• Working with a variety of programming languages.
• Having a solid grasp of statistics, including statistical tests and distributions.
• Staying on top of analytical techniques such as machine learning, deep learning and text analytics.
• Communicating and collaborating with both IT and business.
• Looking for order and patterns in data, as well as spotting trends that can help a business’s bottom line.

Now,let’s see the skills required for Data Scientist:

Skills needed to become Data scientist

• In-depth knowledge of Python coding. It is the most common language including Perl, Ruby etc.
• Sound knowledge of SAS/R
• It is must that Data scientist able to work with unstructured data. Whether it is coming from videos, social media etc.
• Sound skill in SQL database coding.
• Data Scientist should have a good understanding of various analytical functions. For example rank, median etc.
• In depth knowledge of Machine learning requires.
• A Data scientist should familiar with Hive, mahout, Bayesian networks, etc. In data science, knowledge of MySQL is just like an added advantage.

Now, let’s see the roles and responsibilties of Data scientist:

a) A Data Scientist Responsibilities

• Data cleansing and processing.
• Prediction of the business problem. His roles are to give future results of that business.
• Develop machine learning models and analytical methods.
• Find new business questions that can then add value to the business.
• Data mining using state-of-the-art methods.
• Presenting results in a clear manner and doing the ad-hoc analysis.

To know more about the skills and responsibilities of Data Scientist refer below link:

Roles an responsibilities of a Data Scientist

Here is are some job trends of data Scientists.

For performing 3 stages of Data Scientists, 3 categories of tools are needed – tools for pulling data, tools for analyzing the data, and tools for presenting the results.

Different tools for performing the 3 stages of Data Scientists:

1. Tools for data pulling & pre-processing

a. SQL

This is a must skill for all data scientists, regardless of whether you are using structured or unstructured data. Companies are using latest SQL engines like Apache Hive, Spark-SQL, Flink-SQL, Impala, etc.

b. Big Data Technologies

This is the must out of the Skills Needed to Become a Data Scientist. The data scientist needs to know about different big data technologies – 1st Gen technologies like Apache Hadoop & its ecosystem (hive, pig, flume, etc.), Next Gen like – Apache Spark and Apache Flink (Apache Flink is replacing Apache Spark quickly as Flink is a general purpose Big data engine, which can handle real-time stream as well, for more details about Flink follow this comprehensive tutorial).

c. UNIX

As most raw data is stored on a UNIX or Linux server before it’s put in a data-store so it’s nice to be able to access the raw data without the dependency of a database. So Unix knowledge is good for Data Scientists. Follow this command guide to practice Linux commands.

d. Python

Python is a most popular language for the data scientist. Python is an interpreted, object-oriented programming language with dynamic semantics. It is a high-level language with dynamic binding and typing.

2. Tools for Data Analysis & pattern matching

This depends on your level of statistical knowledge. Some tools are used for more advanced statistics and some for more basic statistics.

a. SAS

Lots of companies use SAS, so some basic SAS understanding is good. You can manipulate equations easily.

b. R

R is most popular in the statistical world. R is an open-source tool and language that is object oriented, so you can use that anywhere. It is the first choice of any data scientist as most things are implemented in R. To get the comparison between top data analytics tool, follow this comparison guide between R vs SAS vs SPSS.

c. Machine Leaning

Machine learning is the most demanding and most useful tool the data scientists must have. Machine learning algorithms are used for advanced data analytics, predictive analytics, advanced pattern matching.