Data Science

I Will Prefer The Python Programming Language. Python Is The Best For Starting Your Programming Journey. Here Is The Roadmap Of Python For Logic Building.

Introduction And Basics

• Installation
• Python Org, Python 3
• Variables
• Print Function
• Input From User
• Data Types
• Type Conversion
• Why Python For Data Analysis And Data Science
• How To Install Anaconda
• Running Few Sample Programs Using Python

Operators

• Arithmetic Operators
• Relational Operators
• Bitwise Operators
• Logical Operators
• Assignment Operators
• Compound Operators
• Membership Operators
• Identity Operators

Conditional Statements

• If Else
• If
• Else
• El If (Else If)
• If Else Ternary Expression

While Loop

• While Loop Logic Building
• Series Based Questions
• Break
• Continue
  
• Nested While Loops
• Pattern-Based Questions
• Pass
• Loop Else

Lists

• List Basics
• List Operations
• List Comprehensions / Slicing
• List Methods

Strings

• String Basics
• String Literals
• String Operations
• String Comprehensions / Slicing
• String Methods

For Loops

• Range Function
• For Loop
• Nested For Loops
• Pattern-Based Questions
• Break
• Continue
• Pass
• Loop Else

Functions

• Definition
• Call
• Function Arguments
• Default Arguments
• Docstrings
• Scope
• Special Functions Lambda, Map, And Filter
  
• Recursion
• Functional Programming And Reference Functions

Dictionary

• Dictionaries Basics
• Operations
• Comprehensions
• Dictionaries Methods

Tuple

• Tuples Basics
• Tuples Comprehensions / Slicing
• Tuple Functions
• Tuple Methods

Set

• Sets Basics
• Sets Operations
• Union
• Intersection
• Difference And Symmetric Difference

50+ Coding Ǫuestions Practice And Assignments 

• Numpy
• Pandas
• Matplotlib
• Seaborn
• Sklearn

Data Visualization Using Matplotlib And Seaborn

• Introduction To Matplotlib
• Basic Plotting
• Properties Of Plotting
• Sub Plots
• Line Plots
• Pie Chart
• Bar Graph
• Scatter Plot
• Histograms
• Box Plots
• Violin Plots
• Dist Plots
• Dis Plots
• Kde Plots

• Uni-Variate Analysis
• Bi-Variate Analysis
• Multi-Variate Analysis

• Managing Data Frames With The Dplyr
• Package
  
• Control Structures
• Functions
• Lexical/Dynamic Scoping
• Loop Functions
• Debugging
• Data Visualization In R
• Storytelling With Data
• Principle Tenets
• Elements Of Data Visualization
• Infographics Vs Data Visualization
• Data Visualization & Graphical Functions In R
• Plotting Graphs
• Customizing Graphical Parameters To Improvise The Plots
• Various Guis
• Spatial Analysis
• Other Visualization Concepts

Introduction

• What Is Data
• Types Of Data
• What Is Database
• Types Of Databases
• What Is A Table

Operations On Table

• Creation Of Table
• Select Statement

Data Modifications

• Insert Data Into Tables
• Insert Statements & Insert Into Select Statements
• Updating Existing Data
• Deleting Data From Table
• Drop
• Truncating Tables
• Alter Statements

Constraints

• Primary Key
  
• Foreign Key
• Unique Key
• Not Null
• Check
• Default

Indexes

• Understanding Indexes And Their Importance
• Creating And Dropping Indexes
• Index (B Tree, Hash)

Views

• Creating Views
• Modifying Views
• Dropping Views
• Updating Data Through View

Data Retrieval

• Retrieving Data From A Single Table
• Where Clause
• Group By Clause
• Having Clause
• Order By Clause
• Limit Clause
• Case Statement
• Case Statement For Validating The Data Based On Condition

Joins

• Inner Join
• Left Join
• Right Join
• Full Join
• Cross Join
• Self-Join

Functions

• Numerical Functions
• Date Functions
• String Functions
• Aggregate Functions

Set Operator

• Union
  
• Intersect
• Minus
• Except
• Union All

Sub Ǫueries

• Single Row
• Multiple Row
• Scalar Row
• Correlated
• Exist
• Not Exist
• From And Select
• Where And From

Stored Procedure

• Create
• Dml
• Tcl (Commit,Roll Back, Savepoints,Acid)
• Cursor
• Execution
• Passing Parameters To Stored Procedures And Functions
• Invoking Stored Procedures And Functions

Advance Sǫl Topics

• Windows Functions
• Common Table Expression
• Recursion
• Pivot And Unpivot Operation
• Dynamic Sǫl

• Introduction Of Power Bi Pbi Desktop Installation Power Bi Desktop & Power Bi Service Overview
• Power Ǫuery Software Overview
• Power Pivot Software Overview
• Power View Software Overview
• Power Bi Service Overview
• Power Bi Desktop User Interface
• Building Blocks Of Power Bi
  
• Power Ǫuery
• Data Processing, Data Types And Filters In Power Ǫuery
• Inbuilt Column Transformation
• In Built Row Transformations
• Combine Ǫueries (Merge Ǫueries & Append Ǫueries)
• Merge Ǫueries / Join Tables
• Append Ǫueries / Union All Tables
• Ǫuery Options
• How Tab Options
• Transform Tab Options
• Add Column Tab Options
• Power Pivot
• Power Bi Data Modeling – Model View (Previously Relationship View)
• Enhancing The Data Model- Dax
• Dax Function – Categories
• Dax Text Functions
• Dax Logical Functions
• Dax Date & Time Functions
• Dax Filter Function
• Dax Math And Statistical Functions
• Dax Time Intelligence Functions
• Ǫuick Measures
• Power View
• Report View
• Visuals Interactions
• Filters In Power View
• Hierarchies And Drill-Down Reports
• Power Bi Visualizations
• Visuals For Filtering
• Visualizing Categorical Data
• Visualizing Trend Data
• Visualizing Kpi Data
• Visualizing Tabular Data
• Visualizing Geographical Data
• Grouping, Binning & Sorting
• Bookmarks, Selection Pane & Buttons
  
• Power Bi Services
• Adding Dataset To Power Bi Service And Creating Multiple New Reports
• Dashboards Development

Python Supports N-Dimensional Arrays With Numpy. For Data In 2 Dimensions, Pandas Is The Best Library For Analysis. You Can Use Other Tools But Tools Have Drag- And-Drop Features And Limitations. Pandas Can Be Customized As Per The Need As We Can Code Depending Upon The Real-Life Problem

1.  Stastistics And Maths

Types Of Variables

• Nominal/Categorical
• Ordinal
• Interval/Ratio
• Continuous, Time Series

Central Tendency

• Mean
• Median
• Mode
• Interquartile Mean

2. Measures And Statistical Difference

• Variance
• Correlation
• Standard Error
• Iqr
• Range
• Mean Absolute Difference
• Median Absolute Deviation
• Skewness
• Kurtosis
• Correlation And Auto Correlation And Correlation Matrix
• Correlation Ratio

3. Hypothesis Testing

• Chiquare
• Z Test
• T Test
• Anova
• P Value
  
• Beta Test
• F Score

Data Preprocessing And Feature Engineering

1. Methods Of Imputation

• Mean
• Median
• Mode
• B-Fill
• F-Fill
• Knn Imputation
• Random Forest Imputation
• Regressor Based Imputation

2. Encoding

• Label Encoding
• Dummy Encoding
• Effect Encoding
• Binary Encoding
• Hash Encoding
• Base N Encoding

3. Feature Scaling

• Standardization
• Normalization

4. Handling With Outliers

• Z-Score
• Iǫr
• Percentil

5. Supervised Learning Regression

• Linear Regression
• Polynomial Regression
• Classification
• Naïve Bayes
• Logistic Regression
• Knn
• Decision Tree Svm

6. Unsupervised Learning

• K-Means
• C-Means
• Pca
• K-Means
• C-Means
• Pca

7. Ensemble Models

• Random Forest Classifier
• Random Forest Regressor
• Ada Boost Classifier
• Ada Boost Regressor
• Xg Boost Classifier
• Xg Boost Regressor
• Cat Boost Classifier
• Light Gbm
• Gradient Boost Classifier
• Gradient Boost Regressor
• Voter Classifier
• Stacking
• Customised Ensemble Models

8. Metrics

• Classification Report
• Confusion Matrix
• Accuracy Score
• Crosstab
  
• Fi-Score
• Precision
• Recall
• Roc Curve
• R Squared And Adjusted R Squared
• Rmse, Mse
• Evaluation Metrix

9. Over Sampling And Under Sampling

• Random Over Sampling
• Random Under Sampling
• Smote Over Sampling
• Smote Under Sampling (Knn,Svm,Clustering)
• Random Over Sampling
• Random Under Sampling
• Smote Over Sampling
• Smote Under Sampling (Knn,Svm,Clustering)

10. Cross Validation

• K-Fold Cross Validation
• C-Fold Cross Validation

11. Hyper Parameter Tuning

• Grid Search Cv
• Randomized Cv
• Optuna

Text Processing

• Raw Text
• Noise Entity
  1. Removal Stop
  2. Word
  3. Punctuations
  4. Text Normalization
  5. Urls
  6. Numbers
• Word
  1. Normalization
  2. Tokenization
  3. Stemming
  4. Lemmatization
• Word
  1. Standardization
  2. Regular Expression
• Modified Text
• Feature Engineering
  1. N-Grams
  2. Bag Of Words Count Vectorizer
  3. Tfidf
  4. Gensim
  5. Word2vec
  6. Topic Modeling
  7. Lda
  8. Parts Of Speech Tagging
  9. Dependency Parsing
  10. Constituency Parsing
  11. Named Entity Recognition
  12. Fuzzy Search
  13. Sentiment Analysis
  14. Spacy
  15. Text Blob

• Neural Networks
• Min, Max, Mean Pooling
• Padding
• Basic Neural Network
• Perceptron
• Feed Forward Neural Network
• Back Propagation
• Ann
• Cnn
• Rnn
• Gan

• Live Kaggle Competition
• Hands On Experience On Datasets
• End To End Unique Projects