What is Data Science?

• Data Science is all about using various techniques, and 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 in order to process such massive data sets, Big Firms, Companies are hunting for good data scientists to extract valuable data sets and use them for various business strategies, models, and plants.

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 basically the grammar.

• Statistics is basically the method of analyzing and 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, such as CSV files, and scraping data from websites, using the beautiful-soup python library.

Step 4: Data Cleaning

• These are the steps where most of the time is being spent as a Data Scientist.

• Data Cleaning is all about obtaining the data, fit for doing work and 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:

1. Data Analysis using Pandas and Numpy
2. Data Manipulation
3. Data Visualization

Step 6: Machine 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.

Step 7: Deep Learning

• Deep Learning on the other hand is an advanced version of Machine Learning or is a subset of Machine Learning.

• Which deploys the use of Neural Networks, a framework that combines various machine learning algorithms for solving various tasks, for training data.

Step 8: Learn Deploying of Machine Learning Model

• Deployment is basically 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 9: Real World Testing

• Testing is an important step in Data Science for keeping the efficiency and effectiveness of the ML model in check.

Step 10: 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 and techniques.