Data and Information - Introduction to the Data , Differentiating between Data and Information, Structure Data, Semi Structured Data , Unstructured Data. Types of Analysis - Introduction to Data Analysis, Descriptive Data Analysis , Diagnostics Data Analysis,Predictive Data Analysis , Prescriptive Data Analysis , Decision Making.

Framework and Data Processing Methods - Overview of end to end process,V-Lookup (Merging two data) , Handling Missing Value , Data Formatting , Quartile (handling outlier), Feature Engineering. Tools and Technologies - Overview of Spreadsheet , SQL,Python , Tableau , Machine Learning Algorithms.

Covid Dataset

Operation Of Spreadsheet - Advantages and use of Spreadsheet , Overview of the operation of Spreadsheet, Sorting , Filtering , Conditional Formatting , Handling Duplicates etc. Function of Spreadsheet - Overview of the functions : Sumif, countif, IF, Count , Charts etc.

Introduction to the Business problem - Introduction to the company with the available dataset, Business problem explanation , Description of the Data points. Data Processing : Merging Data - V-Lookup Function , Syntax , Errors of V-lokup , Drawback. Data Processing : Missing Values - Introduction to the Missing values, Missing Value representation , Imputation Method , Average, Standard Deviation , Median

Data Processing: Handling Outliers - Introduction to Outliers , Quartile Function, Candlestick Chart , Finding Outliers. Data Processing : Data Formatting - Data Representation, Formatting of data point. Data Processing: Feature Engineering - Introduction to Feature Engineering and its impact on analysis

Data Analysis Methods - Introduction to the type of data , Univariate Method ,Bivariate Method ,Multivariate Method. Univariate Methods and Analysis - Continuous Value : Trim mean , Density plot, Frequency table. Categorical value : Pivot Table.

Bivariate Methods and Analysis - Continuous to Continuous Method, Categorical to Categorical Method, Categorical to Continuous Method, Scatter Plot , Correlation.

Framework of Storytelling - Storytelling for Business Problem.

Step by Step analysis of the Business Problem - Data processing , Univariate Analysis, Bivariate Analysis.

Analysis of World Economic Data .

Introduction to Database - Database Definition , Database type , RDBMS Database. Introduction to SQL - Introduction to SQL , Database creation, Table creation , Create,Update, Delete , Alter.

Retrieving and Filtering the Data - Select and From keyword , Where Clause. Aggregation Operation - SUM,MIN,MAX,AVG ,COUNT.

Group by operation - Group by clause, having clause ,Order by Clause ,IN, BETWEEN, LIKE .

Introduction to Join - Different types of Join : Inner Join, Outer Join , Cross Join, Left Join, Right Join. Its applications.

Supermarket Data Analysis.

Introduction To Python - First Program in Python, Anaconda and Jupyter Notebook, Variables in Python, Data Types, Python Numbers, Limit of Integers, Arithmetic Operators, Taking Inputs.

Conditionals and Loops - Boolean Datatype, Introduction to If-Else, Using Relational and Logical Operators, Using Else If, Nested Conditionals, While Loop, Primality Checking, Nested Loops. Patterns - Introduction to Patterns, First Patterns, Square Patterns, Triangular Patterns, Character Patterns, Inverted Pattern, Reversed Pattern, Isosceles Pattern. More on Loops - For loop & Range Method, Print Multiples of 3, Check if a Number is Prime, Pattern, Break Keyword, Else keyword with loops, Continue keyword, Pass statements

Functions - Functions and how to use them, Why do we need functions, How does function calling works, Functions using strings & lists, Swap Alternate, Scope of Variables, Default parameters in functions. Object-Oriented Programming Systems(OOPs) - Introduction, Create class & object, Instance Attributes, Class Attributes, Methods, Instance Methods, Constructors, Access modifiers, Class Methods & Static Methods.

Strings, List & 2D List - Strings Introduction, Strings inbuilt functions, Strings slicing, Lists Introduction, List inbuilt functions, Taking Input, Difference of Even-Odd, List Slicing, Multi-dimensional Lists. Tuples, Dictionary, and Sets - Tuples, Tuples Functions, Variable-length input and output, Dictionary Intro, Access/looping elements in dictionary, Adding Or Removing Data In Dictionary, Print All Words With Frequency K, Sets Intro, Functions in sets, Sum Of All Unique Numbers In List.

Working With FileS - Introduction, Open and read Text files, Read file line by line, CSV Files, Work with CSV Files, DictReader, Countrywise Killed. NumPy - Introduction, Why NumPy is fast, Create NumPy arrays, Slicing & Indexing, Mathematical Operations - 1D, Boolean Indexing - 1D, Boolean Indexing - 2D, NumPy Broadcasting. Pandas - Introduction to Pandas, Accessing Data in Pandas, Manipulating Data in Data Frame, Handling NAN, Handling Strings in Data. Matplotlib - Plotting Graphs, Customising Graph, Bubble Chart, Pie Chart, Histogram, Bar Graph, How to decide Graph Type. Seaborn - Categorical Distribution Plots, Categorical Scatter plots, Plotting with Categorical Data, Visualizing Statistical Relationships - ScatterPlot, Seaborn vs Matplotlib, Introduction to Seaborn, Starting with Seaborn, Visualizing Statistical Relationships - LinePlot.

Statistics - Introduction of Statistics, Data Types in Statistics, Sample & Population, Simple Random Sampling, Stratified sampling, Cluster sampling, Systematic Sampling, Categories of Statistics. Descriptive Statistics - Measures in Descriptive Statistics, Measures of central tendency, Measures of Spread, Range & IQR, Variance & Standard Deviation, Measure of Position. Introduction to Inferential Statistics - Introduction to Inferential Statistics, Why Inferential Statistics?, Probability Distribution, Normal Distribution, Standard Normal Distribution, Sampling Distribution, Central Limit Theorem. Hypothesis Testing - What is Hypothesis Testing, Null & Alternative Hypothesis, Significance Level, Test statistic, Test Statistic: Critical value & Rejection Region, Test Statistic: Type of Test, Errors in Hypothesis Testing.

Supermarket Data Analysis.

Introduction to Data Visualisation - Different ways for Data Visualisation, Types Of Data Visualisation, What is Data Visualisation? Importance Of Data Visualisation. Introduction to Tableau - Automatically Generated Fields, Dimension & measure, Tableau Navigation, Data Joins and Union, Connect with Data, Tableau Installation, What is Tableau, Data Types. Tableau Visualisations - Histogram, Bar Chart, Area Chart, Adding customization, Let’s create the First plot, Understanding the Basics of Plotting, Types of charts, Line Chart

Introduction to Machine Learning - Introduction to Machine Learning, Supervised Learning, Steps for Supervised learning, Loading Boston Dataset, Training an Algorithm.

Introduction to Linear Regression - Introduction to Linear Regression, Optimal Coefficients, Cost function, Coefficient of Determination, Analysis of Linear Regression using dummy Data, Linear Regression Intuition. Logistic Regression - Handling Classification Problems, Logistic Regression, Cost Function, Finding Optimal Values, Solving Derivatives, Multiclass Logistic Regression, Finding Complex Boundaries and Regularisation, Using Logistic Regression from Sklearn.

Decision Trees - 1 - Decision Trees, Decision Trees for Interview call, Building Decision Trees, Getting to Best Decision Tree, Deciding Feature to Split on, Continuous Valued Features. Decision Trees - 2 - Code using Sklearn decision tree, information gain, Gain Ratio, Gini Index, Decision Trees & Overfitting, Pruning. Project: Decision - Decision Tree Implementation. Random Forests - Introduction to Random Forests, Data Bagging and Feature Selection, Extra Trees, Regression using decision Trees and Random Forest, Random Forest in Sklearn.

PCA - 1 - Intuition behind PCA, Applying PCA to 2D data, Applying PCA on 3D data, Math behind PCA, Finding Optimal Number of Features, Magic behind PCA. PCA - 2 - PCA on Images, PCA on Olevitti Images, Reproducing Images, Eigenfaces, Classification of LFW Images. Project - Cifar10