The Complete Guide To Becoming A Data Scientist: What You Need To Know.
Table of Contents
What Is Data Science?
Data Science is all about “using various techniques, algorithms to analyze large amounts of datasets (both structured & Unstructured), to extract useful data insights, thus applying them in various business domains.”
Why there’s a demand for Data Scientists?
Data is being generated day by day at a massive rate and to process such massive data sets, Big Firms, Companies are hunting for good data scientists to extract valuable data insights from these data sets and use them for various business strategies, models, and plans.
Also, Read A Simple Guide To Understand Data Structures.
How to Become a Data Scientist?
Finally, let’s dive into the steps to becoming a Data Scientist.
Step 1: Learn Python
- Python is the most common coding language, used by the Majority of Data scientists.
- Because of its simplicity, versatility, and being pre-equipped with powerful libraries useful in data analysis and other aspects of Data Science.
Step 2: Learn Statistics
If Data Science is a language, then statistics is the grammar.
Statistics is the method of analyzing, interpretation of large data sets.
Step 3: Data Collection/ Learn SQL
This is one of the key and important steps in the field of Data Science. This skill involves knowledge of various tools to import data from both local systems, as CSV files, and scraping data from websites, using beautiful soup python library.
Step 4: Data Cleaning
This is the Step where most of the time is being spent as a Data Scientist. Data cleaning is all about obtaining the data, fit for doing work& analysis, by removing unwanted values.
Step 5: Exploratory Data Analysis
Exploratory data analysis is the essential part when talking about data science. The data scientist has many tasks including:
- Data Analysis using Pandas and Numpy
- Data Manipulation
- Data Visualization
Step 6: Machine Learning & Deep Learning
- Machine learning is the core skill required to be a Data Scientist. Machine learning is used to build various predictive models, classification models, etc
- Deep Learning on the other hand is and an advanced version of Machine Learning which deploys the use of Neural Network, a framework that combines various machine learning algorithms for solving various tasks, for training data.
Step 7: Learn Deploying of ML model
Deployment is the process of making your Machine Learning Model available to end-users for use. This is achieved by the integration of the model with various existing production environments.
Step 8: Real-World Testing
Testing is an Important Step In Data Science for keeping the efficiency and effectiveness of the ML model In check.
Step 9: Analytical Curiosity
The data science field is a field that is evolving at a higher pace, therefore it requires inbuilt curiosity to explore more about the field, regularly updating and learning various skills & techniques.
Books to Read
Data Science from Scratch by Joel Grues – Available on Amazon
Hands-On Machine Learning with Scikit-Learn, Keras and Tensor Flow: Concepts, Tools and Techniques to Build Intelligent Systems – Available on Amazon
