How To Become A Data Scientist & What It Takes

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Written By: Muhammad Shahzeb ( Researcher, Full Stack Developer )
The ultimate learning path guide detailing all the training, knowledge, and skills that you need to become a data scientist.

data scientist

If you looking for a career which must be

  • Challenging,
  • Promising,
  • Lucrative field
  • High in-demand,
  • Interesting and more

So, you can become a data scientist because data science and data analysis can fulfill all of your requirements. Data science has become a high trended topic in the past few years but the question is what technologies we will learn and become a successful data scientist in 2020.

As we all know we are in the middle of the fourth (4th) industrial revolution which is driven by Artificial intelligence and the internet of things. Both are characterized by the collection of data, analysis of data, and exchange of data so there is no chance of doubt that there will need data scientists for handling this data.

Even not only here but data science are in high and growing demand in many other companies such as

  • Products Manufacturers
  • Internet retailer
  • Tech start-ups
  • Government sectors for making policies based on data
  • Pharmaceutical companies
  • Marketing companies for making marketing strategies based on customer data

So how do you establish a career as a data scientist? What different technologies need to be learned? we will see steps by steps:


Should I Become A Data Scientist Or Not


Programming Languages

You need to learn a basic programming language concept. Different programming languages are popular for different purposes such as Python is for dynamic, easiest, general-purpose, and widely-used programming language for data science.
R is one of the most important languages within the data science community that provides support for statistical computing and graphics.
Structured query language (SQL) is also popular in the data science field. The structural query language used for querying the information that is stored in a relational database for editing, deleting, etc.
Julia is placed in high-level programming languages that is best for numerical analysis with high performance.
So, you need to have a good understanding of high-level languages

  • Python
  • Java
  • R
  • C sharp

Machine Learning

Machine learning is an in-demand application of artificial intelligence that capable the system for learning automatically and enhance the experience without programming explicitly. This technique mainly focuses on data science to develop such a computer program that can acquire data, process it, and learn from this data.
It is very important for making different models so you should be a hands-on experience of these concepts such as

  • Classification
  • Regression
  • Reinforcement learning
  • Deep learning
  • Dimensionality Reduction
  • Clustering

IDE

An integrated development environment (IDE) is a software that facilitates programmers for writing code. IDE is also called a source code editor. To become a data scientist, you should have hands-on experience in

  • PyCharm
  • Jupiter
  • Spyder
  • R-studio
  • Net beans
  • Visual studio

Data Analysis

Data analysis includes the process of Data Requirement Gathering, collection of data, cleaning the data, transforming the data, and modeling of data to extract useful information for making decision.so you should learn different techniques of data analysis.

  • Feature engineering
  • Data wrangling
  • Exploratory data analysis (EDA)

There are many types of data analysis based on business and technology such as predictive, text, diagnosis, statistical, and prescriptive analysis.

Data visualization

Data visualization is the process of representation of data graphically. With the help of different visual elements for example graphs, charts, and maps, we can easily understand the patterns in data. There are different data visualization tools which provide these facilities such as

  • Power BI
  • High charts
  • Data wrapper
  • Tableau’s biggest competitor
  • Fusion Charts
  • Si-sense

Math

Math is crucial and basic for data science it is very important you have a good understanding of different math concepts such as

  • Statistics
  • Linear algebra
  • Differential Equation
  • Calculus

Web Scraping

The bulk of data is present on the internet in the form of websites for example if you need to make some marketing strategies so you should need customers’ data that what people mostly purchased, what not, or why not. For this, you scrape data from different eCommerce websites. This is a very simple example of showing the importance of acquiring data.
Here web scrapers come and play an important role for gathering and extraction of data by using different techniques APIs etc. and save into a local file in computer or database server for further processing.

  • Urllib
  • Selenium
  • Scrappy
  • Python Requests
  • Mechanical Soup
  • LXML
  • Selenium
  • Beautiful Sop

Deploy

When you build your data science solution then you will need to deploy in a server or centralize system where multiple remote users can interact. For that, you can use

  • Amazon web services (AWS)
  • Azure

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