A Self-Guide || How to Become a Data Scientist and Data Analyst || Pt - #0

Here is a broad path to becoming a data scientist or analyst, along with some important areas to concentrate on:

Map for Data Scientist

Programming

• Pick a programming language (such Python, R, or Java, Julia) and become familiar with the fundamentals.
• Learn how to use tools like NumPy, Pandas, and Scikit-Learn to handle and analyse data.

Basics of Mathematics

• Differential calculations, probability, statistics, linear algebra, and discrete mathematics.

Data Analysis 

• Learn how to extract and alter data from databases using SQL.
• Learn how to visualise data using programmes like Tableau and Excel.

    ( Feature Engineering, Data Wranging and EDA).

Machine Learning

• Discover the many kinds of machine learning algorithms and how to use them.
• Discover how to create and assess machine learning models.

    ( Classification, Regression, Reinforcement Learning, Deep Learning, Dimensionality                 Reduction and Clustering ) 

Web Scrapping

• Gain knowledge and experience in a specific industry or domain

    ( Beautiful SOAP, Scrappy and URLLIB )

Visualisation

    ( Tableau, D3.js, Scatter Plot, Power BI and ggplot2 )

Communication and presentation skills

• Learn how to effectively communicate your findings to stakeholders and decision makers

How to Become a Data Analyst: A Self-Guide

Maths and Stats

• Statistics and Probability 

• Algebra and Linear Algebra

• Calculus and Discrete Mathematics

Excel (Basic - Intermediate)

• Editing Text and Formulas

• Excel Functions and Lists

• Worksheets and Pivot Tables

• Formatting Data and Data Validation

• Working with Charts and Templates

• Lookup, Macros, and VBA

Python Programming

• Syntax and Basics

• Data Structures and Algorithms

• Pandas  Numpy

• Scipy  Matplotlib

SQL and Database

• DBMS, Normalisation and ERD

• SQL Syntax, data types, variables, Select, Where, And, Or, Not

• Insert, Update, Delete, Min, Max, Count, Average, Sum

• Like, In, Between, Top, Group By, Order By, having, exists, any, all, case

• Joins such as Inner, Outer, Left, Right, Full, Self joins.

• Database-related, table commands such as create, alter, update, drop

• SQL constraints such as not null, unique, check, default, auto-increment

• Views, Triggers, Functions, PL/SQL, Injection, Hosting

Power BI / Tableau

•  Querying & Transforming Data

•  Data Modelling

•  Calculations and Formula

• Reports & Visualisations

•  Dashboards

Data Preparation and Validation

• Data collection 

• Data discovery and profiling 

• Data cleansing

• Data transformation

• Data validation and publishing

Exploratory Analysis and Modeling

• Regressions

•  Classification 

•  Clustering

 Machine Learning Libraries

• Scikit-Learn

• PyTorch 

• TensorFlow

Data Storytelling

• It is about using human communication to help an

• audience develop a connection to that information.