How to Become Data Scientist ASAP. Today in this blog I am going to share 8 simple steps which are needed to become Data Scientist.
Before I start just want to share some basics of Data Scientist like What Data Scientist Is ? What are its Responsibilities ?
What Data Scientist Is ?
Data scientist is the person who performs well in statistics than any software engineer and good in software engineering than any statistician.
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Data Scientist Responsibilities
- Require to do undirected research and to form open-ended industry questions.
- Require to extract large volumes of data from multiple resources including external and internal.
- Make use of advanced analytics programs, statistical methods and machine learning to make data for the use in prescriptive and predictive modeling.
- Discard irrelevant data by thoroughly clean and pruning data.
- Observe the data from multiple angles for determination of any hidden weaknesses, trends and opportunities.
- Formulate the data driven solutions to the most pressing challenges.
- Find out the new algorithms to solve the problems and to make new tools for automation of work.
- To establish proper communications with management and IT departments for the purpose of communicating findings and predictions by means of data visualization and reports.
- Suggest cost-effective changes to existing strategies and procedures.
Next is How to Become Data Scientist ASAP i.e.
8 Data skills required to become Data Scientist:
1) Basic Tools:
You should well aware about the use of tools of trade regardless of the company for which you are going for interview. It means you should have knowledge of statistical programming language such as Python or R and database querying language such as SQL.
2) Basic statistics:
Basic statistics understanding is must for securing data scientist position. Even the people who interview for data scientist position not able to answer the simple question of p value definition.
Hence you need to familiar with statistical tests, distributions, maximum likelihood estimators and so on. Your statistical knowledge should be enough to decide whether particular technique is a valid approach or not.
For all types of companies statistics is important but mainly for data driven companies where product is not data focused and stakeholders of product seek your help for making decisions and design/evaluate experiments.
3) Machine learning:
If you are a part of company that contain huge amount of data or in that company product itself is data driven then you must familiar with machine learning methods.
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It means you need to aware about k nearest neighbors, random forests, ensemble methods and so on. Most of these techniques developed by using Python or R libraries.
It is important know about the broad strokes and when to use the different techniques.
4) Multi variable Calculus and Linear Algebra:
If you are going for an interview then there may be chances of questions related to the multi variable calculus and linear algebra because they form the basis of most of the techniques.
It is important for the data scientist to have the knowledge in this field as sometimes data scientist carry out the implementation in own house at that time this knowledge works.
For the companies where product defined by the data and small improvements in algorithm optimization and predictive performance bring the huge win to the company this concept knowledge play very important role.
5) Software Engineering:
If you are going for an interview in a small company and you are the first data scientist for the company then strong software engineering background is very important.
You will be responsible for handling data logging and development of data driven products.
6) Thinking like a Data Scientist:
Companies want you to be a data driven problem solver. At some point in interview you may asked some high level problems like test the company who want to run data driven product.
At this time it becomes important to think about what things are important and what are unnecessary. How to interact with the product managers and engineers as a data scientist. What methods you have to use. When to make the approximations.
7) Data Munging:
Since the data you are working or analyzing going to be messy and difficult to work with. Therefore it is very important to know about how to deal with this type of data imperfections.
Imperfections occurs due to missing values, inconsistent string formatting e.g. ‘New York’ versus ‘new York’ versus ‘ny’ and date formatting like ‘2017-06-06’ vs ‘06/06/2017’ ,Unix time vs time stamps and so on.
This will be very important for the small companies where you are the first data scientist hire or in the companies where product is not related to data but everyone should have these skills.
8) Data Visualization & Communication:
Communicating and visualizing of data is very important basically in the new companies who are in the process of making data driven decisions for the first time or where the data scientist treated as person who help others in making data driven decisions.
If we talk about communicating then it defines your findings and way techniques to work with audiences both technical as well non technical.
In visualization it requires to familiar with data visualization tools like ggplot and d3.js.It is important to familiar with the tools of visualization but also have knowledge about the principles behind visually encoding data and communicating information.
Data Scientist Salary
Average Salary (2015): $118,709 per year
Median Salary (2015): $93,991 per year
Total Pay Range: $63,524 – $138,123
It will take dedication of around 2 months along with your regular job to become Data Scientist.
I struggled lot and finally got some free material which shows step by step process to become Data Scientist.
If you need it then please subscribe us or let me know. I will feel great to help you on the same.
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Author: Poonam Kaushal
A blogger and passionate writer who always try to introduce new concepts to readers.Involved in blogging since 2014 and believe in “No pain No gain” if you have to achieve something then you need to work hard.Working as freelance blogger in many companies and also managing her own blog that you all know savenlike.com.