Data science Learning Path

Hello friends!! I have created the learning path for data science tools. It covers almost all the topics to start with the tool and become comfortable in it. Check the data science learning path below.

Summary

1. MS Excel
2. Tableau
3. SQL
4. Power BI
5. R
6. Python

MS Excel

Stepwise learning path for MS Excel.

1. Enter Data and create basic Tables
2. Start with
3. Excel basic features
4. Excel Basic Formulas
5. Logical Functions in Excel
6. Advance formulas
   • Logical Functions and Error Trapping
   • Date and Time Formulas
   • Financial Formulas
   • Math and Statistical Formulas
   • Lookup, Information, and Text Formulas
   • SUMIF, COUNTIFs
7. How to Create Pivot Table in Excel
8. How to Create Basic and Advanced Charts in Excel
9. Data Analysis tool
10. Automation using Macros and VBA

Tableau

Stepwise learning path for Tableau.

1. Getting Started
   • The Tableau Interface
   • Design Flow
   • File Types
   • Data Types
   • Show Me
   • Data Terminology
2. Connecting with Data
   • Data Sources
   • Custom Data View
   • Extracting Data
   • Fields Operations
   • Relationships
   • Joins
3. Calculations
   • Basic Calculations
   • Creating Calculated Fields
   • Using the TOTAL function to summarize dimensions
   • Operators
   • Functions
   • Numeric Calculations
   • String Calculations
   • Date Calculations
   • Table Calculations
   • LOD Expressions
   • Tableau Essentials: Calculated Fields – Aggregate Functions
4. Sorting and Filtering
   • Basic sorting
   • Basic filtering
   • Advance sorting
   • Advance filtering
5. Creating Visuals in Tableau
   • Basic Visuals (Bar, Line, Pie, Table, Box plots)
   • Mapping (Filled and symbol maps)
6. Advanced charts
   • Waterfall Charts
   • Bump Charts
   • Funnel Charts
   • Box Plot
   • Pareto Charts
   • Histograms and Others
7. Building Dashboard
   • Dashboard and Story Creation
   • Adding views and objects to dashboards
   • Applying Filters to dashboard
   • Various Layouts and Formatting options
   • Interactive Dashboard
   • Dashboard Actions
   • Story Points
8. Publishing the dashboard to cloud
   • Intro to Tableau server
   • Intro to Tableau server online

SQL

Stepwise learning path for SQL.

1. SQL Basics
   • Creating Tables
   • Inserting values
   • Adding a New column in the existing Table
   • Deleting a Column from a Table
   • Deleting the Table
2. SQL start with Commands
   • Learn basic SQL commands: SELECT, FROM and WHERE
   • Learn logical operators in SQL
   • Learn commands: IN , AND, OR, NOT, LIKE, NULL, ISNULL, BETWEEN
3. SQL Joins
   • Learn to join different type of joins
   • LEFT, RIGHT, INNER, OUTER joins
4. SQL Sorting and Grouping
   •  Learn GROUP BY , ORDER BY, HAVING
5. SQL Aggregations and Arithmetic operations
   • Learn Excel Functions: AVG, COUNT, SUM, MIN, MAX, UPPER, LOWER, LENGTH, REPLACE, TRIM, MID
   • Learn to write common aggregations: AVG, COUNT, SUM, MIN and MAX
   • Learn to write DATE function
   • Mathematical and String Functions
6. SQL Update
   • Learn to write CASE
   • Learn to write UPDATE statements
   • MERGE and UPDATE statements
7. SQL subqueries and Temp Tables
   • Learn to write subqueries and multiple queries together  
   • Learn to use temp tables to access table with more than one query
8. SQL Data cleaning
   • Learn to perform data cleaning in SQL  
   • Learn removing duplicates, removing NULLs
9. SQL Window Functions
   • Learn to use WINDOW functions in SQL
   • Aggregate Window Functions: SUM(), MAX(), MIN(), AVG(). COUNT()
   • Ranking Window Functions: RANK(), DENSE_RANK(), ROW_NUMBER(), NTILE()
   • Value Window Functions: LAG(), LEAD(), FIRST_VALUE(), LAST_VALUE()

Power BI

Stepwise learning path for Power BI.

1. Getting Started with Power BI
   • Power BI Interface
   • Design flow
   • Type of data sources
   • Data Terminology
2. Connecting to Data and Intro to Query Editor
   • Type of Data Sources
   • Intro to the Power BI Query Editor
   • Table Transformations
   • Text, Number & Date calculations
   • Index & Conditional Columns
   • Grouping & Aggregating Data
   • Pivoting & Unpivoting
   • Merging & Appending Queries
   • Connecting to Folders and import multiple files
   • Defining Hierarchies & Categories
   • Query Editing & Power BI Best Practices
3. Building Data Models and Relationships
   • Intro to Database Normalization
   • Data (“Fact”) Tables vs. Lookup (“Dimension”) Tables
   • Creating Power BI Table Relationships
   • “Star” vs. “Snowflake” Schemas
   • Active vs. Inactive Relationships
   • Relationship Cardinality
   • Connecting Multiple Data Tables
   • Filtering & Cross-Filtering
   • Hiding Fields from the Power BI Report View
   • Data Modeling & Power BI best Practices
4. Learn Calculated Fields with DAX
   • Intro to Data Analysis Expressions (DAX)
   • Calculated Columns vs. Measures
   • DAX Syntax & Operators
   • Common Power BI Functions
   • Basic Date & Time Formulas
   • Logical & Conditional commands
   • Text, Math & Stats Functions
   • Joining Data with RELATED
   • CALCULATE, ALL & FILTER Functions
   • DAX Iterators (SUMX, AVERAGEX)
   • Quick Measures
   • Measures v/s custom Columns
   • Time Intelligence Formulas
   • DAX & Power BI Best Practices
5. Creating visualizations
   • Intro to the Power BI Report View
   • Adding Basic Charts to Power BI Reports
   • Formatting
   • Filtering Options
   • Matrix Visuals
   • Slicers & Timelines
   • Cards & KPIs
   • Power BI Map Visuals (Basic, Fill, ArcGIS)
   • Tree maps, Lines, Areas & Gauges
6. Other Reporting features and Dashboard Interactions
   • Editing Report interactions
   • Adding Drill through Filters
   • Using “What-If” Parameters
   • Managing & Viewing Roles
   • Power BI Data Viz Best Practices
   • Bookmarks
7. Custom Visuals
   • Using third party Custom Visuals
   • Installing and using a Custom Visual for Power BI
8. Publishing Power BI reports to power BI services

R Programming

Stepwise learning path for R programming

1. Introduction to R
   • Introduction to R
   • Arithmetic operation
   • Variable assignment
   • Basic Data types
   • Vectors, Matrix, List, Array and Data Frames
   • Factors, Factor levels, Ordered factors
2. Data Input and Output in R
   • Intro to import data
   • Reading csv, txt, flat and excel files
   • Saving output
3. Basics of R programming
   • Logical statements
   • If, else, conditional statements
   • Loops in R (for, while)
   • Functions of R
   • Merging Dataframes
4. R programming commands
   • Mathematical function
   • Row and column operations
   • Working with Dates
   • Working with String
   • Aggregation
   • installing and loading packages
   • Inspection data
   • Creating, deleting and manipulating columns
5. Data Manipulation using dplyr package in R
   • Basics of dplyr
   • using select(), arrange(), filter(), summarise(), and mutate() commands
   • Pipe operators
   • Summarizing data using dplyr
   • Pivoting
   • Long to wide format
6. Data Visualization in R
   • Intro to data visualization using base R
   • Basics of ggplot2(Grammar of Graphics) package
   • Creating plots using ggplot2
   • Labels, layers, theme, facets using ggplot2
   • Univariate analysis
7. Data visualization using ggplot2
   • Adjusting coordinates
   • Change themes
   • Legend
   • Grid lines and panel background
   • Plot margin and background
   • Annotation
   • Formatting plots
   • Saving ggplot2 plot
8. Machine learning using R
   • Linear Regression
   • Logistic Regression
   • Clustering
   • Decision Trees, Support vector machines and Random Forest
   • Intro to text analytics(natural language processing)

Python

Stepwise learning path for Python

1. Intro to Python
   • Installing and Setting up environment
   • Install Anaconda and Python
   • Launch a Jupyter Notebook
   • Variables and Operators
   • Booleans and Comparisons
   • expressions and statements
2. Basics of python
   • Functions, Modules and strings
   • Lists, Tuples, Sets, and Dictionaries
   • Installing a Package (Pandas, Scipy, NumPy,Plotly,Seaborn etc.)
3. Data cleaning and manipulation using Pandas, NumPy, Scipy
   • Creating, Reading and Writing Data
   • Indexing, Selecting & Assigning
   • Summary Functions and Maps
   • Grouping and Sorting
   • Data Types and Missing Values
   • Renaming and Combining
   • Handling Missing Values
   • Scaling and Normalization
   • Parsing Dates
   • Character Encodings
   • Inconsistent Data Entry
4. Data Visualization
   • Seaborn, Matlplotlib, plotly
   • Line, Bar, Heatmaps, Scatterplots
   • Histograms, Boxplots
   • Maps
   • Plot styles and custom styling
5. Machine Learning using ScikitLearn
   • Linear Regression
   • Logistic Regression
   • Clustering
   • Decision Trees, Support vector machines and Random Forest
   • Intro to text analytics(natural language processing)

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