Data Analytics + MNC Certified Internship + Job Assistance Program

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"Our Data Analytics program offers practical skills in data analysis, programming, and 15+ hands-on projects for career growth with MNC Certification and Job Assistance."

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Course Curriculum

Programming Basics & Environment Setup

Installing Anaconda, Anaconda Basics and Introduction
Get familiar with version control, Git and GitHub.
Basic Github Commands.
Introduction to Jupyter Notebook environment. Basics Jupyter notebook Commands.
Programming language basics

Strings, Decisions & Loop Control

Working With Numbers, Booleans and Strings, String types and formatting, String operations
Simple if Statement, if-else Statement if-elif Statement.
Introduction to while Loops, for Loops, Using continue and break

Functions & Modules

Introduction To Functions
Defining & Calling Functions
Functions With Multiple Arguments

Python Programming Overview

Python Overview
Python 2.7 vs Python 3
Writing your First Python Program
Lines and Indentation, Python Identifiers
Various Operators and Operators Precedence
Getting input from User, Comments, Multi line Comments

Python Data Types

List, Tuples, Dictionaries
Python Lists, Tuples, Dictionaries Accessing Values, Basic Operations
Indexing, Slicing, and Matrixes
Built-in Functions & Methods
Exercises on List, Tuples And Dictionary

Functions & Modules (continued)

Anonymous Functions - Lambda
Using Built-In Modules, User-Defined Modules, Module Namespaces, Iterators And Generators

Python Programming File I/O And Exceptional Handling and Regular Expression

Opening and Closing Files
Open Function, file Object Attributes
Close() Method, Read, write, seek.
Exception Handling, try-finally Clause
Raising an Exceptions, User-Defined Exceptions
Regular Expression- Search and Replace
Regular Expression Modifiers
Regular Expression Patterns

Fundamentals of Math and Probability

Probability distributed function & cumulative distribution function. Conditional Probability, Bayeâ€™s Theorem
Problem solving for probability assignments
Random Experiments, Mutually Exclusive Events, Joint Events, Dependent & Independent Events

Introduction to Statistics, Statistical Thinking

Variable and its types
Quantitative, Categorical, Discrete, Continuous, all with examples
Outliers, Causes of Outliers, How to treat Outliers, I-QR Method and ZScore Method

Inferential Statistics

Central Limit Theorem
Point estimate and Interval estimate
Creating confidence interval for population parameter
Characteristics of Z-distribution and T-Distribution.
Type of test and rejection region.
Type of errors in Hypothesis Testing

All about Population & Sample

Population vs Sample, Sample Size
Simple Random Sampling, Systematic Sampling, Cluster Sampling, Stratified Sampling, Convenience Sampling, Quota Sampling, Snowball Sampling and Judgement Sampling

Descriptive Statistics

Measures of Central Tendency â€“ Mean, Median and Mode
Measures of Dispersion â€“ Standard Deviation, Variance, Range, IQR (Inter-Quartile Range)
Measure of Symmetricity/ Shape â€“ Skewness and Kurtosis

Hypothesis Testing

Type of test and Rejection Region
Type o errors-Type 1 Errors, Type 2 Errors. P value method, Z score Method. The Chi-Square Test of Independence.
Regression. Factorial Analysis of Variance. Pearson Correlation Coefficients in Depth. Statistical Significance
Null and Alternative Hypothesis One tailed and Two-tailed Tests, Critical Value, Rejection region, Inference based on Critical Value
Binomial Distribution: Assumptions of Binomial Distribution, Normal Distribution, Properties of Normal Distribution, Z table, Empirical Rule of Normal Distribution & Central Limit Theorem and its Applications

Data Processing & Exploratory Data Analysis

What is Data Wrangling
Data Pre-processing and cleaning?
How to Restructure the data?
What is Data Integration and Transformation

Python Programming Overview

Dot Product, Projecting Point on Axis.
Matrices in Python, Element Indexing, Square Matrix, Triangular Matrix, Diagonal Matrix, Identity Matrix, Addition of Matrices, Scalar Multiplication, Matrix Multiplication, Matrix Transpose, Determinant, Trace
T-Test, Analysis of variance (ANOVA), and Analysis of Covariance (ANCOVA) Regression analysis in ANOVA

EDA

Finding and Dealing with Missing Values.
What are Outliers?
Using Z-scores to Find Outliers.
Bivariate Analysis, Scatter Plots and Heatmaps.
Introduction to Multivariate Analysis

Machine Learning Introduction

Definition, Examples, Importance of Machine Learning
Definition of ML Elements: Algorithm, Model, Predictor Variable, Response Variable, Training - Test Split, Steps in Machine Learning
ML Models Type: Supervised Learning, Unsupervised Learning and Reinforcement Learning

Data Preprocessing

Encoding the data: Definition, Methods: OneHot Encoding, Mean Encoding, Label Encoding, Target Guided Ordinal Encoding
Evaluation Metrics for Classification model: Confusion Matrix, Accuracy, Misclassification, TPR, FPR, TNR, Precision, Recall, F1 Score, ROC Curve, and AUC. Using Python library Sklearn to create the Logistic Regression Model and evaluate the model created
Data Preprocessing: Types of Missing values (MCAR, MAR, MNAR), Methods to handle missing values, Outliers, Methods to handle outliers: IQR Method, Z Method, Feature Scaling: Definition, Methods: Absolute Maximum Scaling, Min-Max Scaler, Normalization, Standardization, Robust Scaling

Logistic Regression Model

Definition. Why is it called the 'Regression model'?
Sigmoid Function, Transformation & Graph of Sigmoid Function

K Nearest Neighbours Model

Definition, Steps in KNN Model, Types of Distance: Manhattan Distance, Euclidean Distance, 'Lazy Learner Model'.
Confusion Matrix of Multi Class Classification
Using Python library Sklearn to create the K Nearest Neighbours Model and evaluate the model

Decision Tree Model

Definition, Basic Terminologies, Tree Splitting Constraints, Splitting Algorithms: CART, C4.5, ID3, CHAID
Splitting Methods: GINI, Entropy, Chi-Square, and Reduction in Variance
Using Python library Sklearn to create the Decision Tree Model and evaluate the model created

Hyperparameter Tuning

GridSearchCV, Variable Importance.
Using Python library Sklearn to create the Random Forest Model and evaluate the model created. Use cases

Random Forest Model

Ensemble Techniques: Bagging/bootstrapping & Boosting.
Definition of Random Forest, OOB Score
K-Fold Cross-Validation

Naive Bayeâ€™s Model

Definition, Advantages, Bayeâ€™s Theorem Applicability, Disadvantages of Naive Bayeâ€™s Model, Laplaceâ€™s Correction, Types of Classifiers: Gaussian, Multinomial and Bernoulli
Using Python library Sklearn to create the Naive Bayeâ€™s Model and evaluate the model created

K Means and Hierarchical Clustering

Definition of Clustering, Use cases of Clustering
K Means Clustering Algorithm, Assumptions of K Means Clustering
Sum of Squares Curve or Elbow Curve

Principal Component Analysis(PCA)

Definition, Curse of Dimensionality, Dimensionality Reduction Technique, When to use PCA
Use Cases
Steps in PCA, EigenValues and EigenVectors, Scree Plot.
Using Python library Sklearn to create Principal Components

Hierarchical Clustering

Dendrogram, Agglomerative Clustering, Divisive Clustering, Comparison of K Means Clustering and Hierarchical Clustering
Using Python library Sklearn to create and evaluate the clustering model

Support Vector Machine(SVM)

Model: Definition, Use Cases, Kernel Function, Aim of Support Vectors, Hyperplane, Gamma Value, Regularization Parameter
Using Python library Sklearn to create and evaluate the SVM Model

SQL and RDBMS

RDBMS And SQL Operations.
Single Table Queries - SELECT, WHERE, ORDER BY, Distinct, And, OR
Multiple Table Queries: INNER, SELF, CROSS, and OUTER, Join, Left Join, Right Join, Full Join, Union

NoSQL, HBase & MongoDB

NoSQL Databases
Introduction to HBase
HBase Architecture, HBase Components, Storage Model of HBase
HBase vs RDBMS
Introduction to MongoDB, CRUD
Advantages of MongoDB over RDBMS

Programming with SQL

Mathematical Functions
Variables
Conditional Logic
Loops
Custom Functions
Grouping and Ordering

Advance SQL

Advance SQL Operations
Data Aggregations and summarizing the data
Ranking Functions: Top-N Analysis
Advanced SQL Queries for Analytics

JSON Data & CRUD

Basics and CRUD Operation
Databases, Collection & Documents
Shell & MongoDB drivers
What is JSON Data
Create, Read, Update, Delete
Finding, Deleting, Updating, Inserting Elements
Working with Arrays
Understanding Schemas and Relations

Programming with SQL (continued)

Partitioning
Filtering Data
Subqueries

Introduction to MongoDB

What is MongoDB
Characteristics and Features
MongoDB Ecosystem
Installation process
Connecting to MongoDB database
Introduction to NoSQL
Introduction of MongoDB module
What are Object Ids in MongoDB

Advance SQL

MongoDB Use cases
MongoDB Structures
MongoDB Shell vs MongoDB Server
Data Formats in MongoDB
MongoDB Aggregation Framework
Aggregating Documents
Working with MongoDB Compass & exploring data visually
Understanding Create, Read, Update, Delete
Schemas & Relations
Document Structure
Working with Numeric Data
Working on Scheme Designing

Introduction to Tableau

Creating dashboard pages
Connecting to data source
How to create calculated columns
Different charts

Dashboard and Stories

Working in Views with Dashboards and Stories
Working with Sheets
Fitting Sheets
Legends and Quick Filters
Tiled and Floating Layouts, Floating Objects

Visual Analytics

Getting Started With Visual Analytics
Sorting and grouping
Working with sets, set action
Filters: Ways to filter, Interactive Filters
Forecasting and Clustering

Tableau (Advance)

Mapping
Coordinate points
Plotting Latitude and Longitude
Custom Geocoding
Polygon Maps
WMS and Background Image

Introduction To Hadoop & Big Data

Distributed Architecture - A Brief Overview. Understanding Big Data
Introduction To Hadoop, Hadoop Architecture
HDFS, Overview of MapReduce Framework
Hadoop Master: Slave Architecture
MapReduce Architecture
Use cases of MapReduce

What is Spark

Introduction to Spark RDD
Introduction to Spark SQL and Data frames
Using R-Spark for machine learning

Introduction to Time Series Forecasting

Basics of Time Series Analysis and Forecasting
Method Selection in Forecasting
Moving Average (MA) Forecast Example
Different Components of Time Series Data
Log Based Differencing, Linear Regression for Detrending

Introduction to ARIMA Models

ARIMA Model Calculations, Manual ARIMA Parameter Selection
ARIMA with Explanatory Variables
Understanding Multivariate Time Series and their Structure
Checking for Stationarity and Differencing the MTS

Getting started with Excel

Creating a New Workbook
Navigating in Excel
Moving the Cell Pointer
Using Excel Menus
Creating Headers, Footers, and Page Numbers
Adjusting Page Margins and Orientation
Adding Print Titles and Gridlines, rows to repeat at top of each page
Formatting Fonts & Values
Adjusting Row Height and Column Width
Changing Cell Alignment
Adding Borders
Applying Colors and Patterns
Using the Format Painter
Merging Cells, Rotating Text
Using Auto Fill

Switching Between Sheets in a Workbook

Inserting and Deleting Worksheets
Renaming and Moving Worksheets
Protecting a Workbook
Hiding Columns, Rows and Sheets
Splitting and Freezing a Window
Using Excel Toolbars: Hiding, Displaying, and Moving Toolbars
Entering Values in a Worksheet and selecting a Cell Range
Previewing and Printing a Worksheet
Saving a Workbook & Re-opening a saved workbook
Splitting and Freezing a Window
Inserting Page Breaks
Advanced Printing Options

Entering Date Values and using AutoComplete

Editing, Clearing, and Replacing Cell Contents
Cutting, Copying, and Pasting Cells
Moving and Copying Cells with Drag and Drop
Collecting and Pasting Multiple Items
Using the Paste Special Command
Inserting and Deleting Cells, Rows, and Columns
Using Undo, Redo, and Repeat
Checking Your Spelling
Finding and Replacing Information
Inserting Cell Comments
Creating a basic Formula
Cell Referencing
Calculating Value Totals with autoSum
Editing & Copying Formulas
Fixing Errors in Your Formulas
Formulas with Several Operators
Cell Ranges
Conditional Formatting
Working with the Forms Menu
Sorting, Subtotaling & Filtering Data
Copy & Paste Filtered Records
Using Data Validation
Changing a Chart Type and Working with Pie Charts
Adding Titles, Gridlines, and a Data Table
Formatting a Data Series and Chart Axis
Using Fill Effects
Using Excel Toolbars: Hiding, Displaying, and Moving Toolbars
Entering Values in a Worksheet and Selecting a Cell Range
Previewing and Printing a Worksheet
Saving a Workbook & Re-opening a saved workbook

Creating & Working with Charts

Creating a Chart
Moving and Resizing a Chart
Formatting and Editing Objects in a Chart
Changing a Chart's Source Data

Data Analysis & Pivot Tables

Creating a PivotTable
Specifying the Data a PivotTable Analyzes
Changing a PivotTable's Calculation

Getting Started With Power BI

Installing Power BI Desktop and Connecting to Data
Overview of the Workflow in Power BI Desktop
Introducing the Different Views of the Data Model
Query Editor Interface
Working on Data Model

Programming with Power BI

Working with Time Series
Understanding aggregation and granularity
Filters and Slicers in Power BI Maps
Scatterplots and BI Reports
Connecting Dataset with Power BI
Creating a Customer Segmentation Dashboard
Analyzing the Customer Segmentation Dashboard

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Experiences shared by our Alums

"I would like to express my gratitude to Mr. Vijay Sir, MIND2I, Banglore. Despite having no prior programming knowledge after completing my B-Tech in EEE, I decided to join the Python course under his guidance. His teaching method, starting from the basics and progressing to advanced concepts, has been instrumental in transforming me into a proficient Python developer. The sessions were enjoyable, and I genuinely appreciate his excellent training. Thank you for the valuable learning experience."

Sandeep

Data Engineer, Accenture

Bengaluru,India

"I will highly recommend this certification to either beginners or intermediate level folks who are looking to start a career in Python or Data Science. The overall duration or course is interactive and thanks to Vijay sir youâ€™ll gain all basic knowledge easily."

Rawat

Data Analyst, Infosys

Bengaluru,India

"Mind2I is very clear in its explanation of concepts. It goes in depth with the things that are important to know. Mind2I teaching methods has helped me to understand concepts and encouraged me to learn more. If any one wants to learn about Data Science, I highly recommend Mind2I for coaching."

Alisha Rafique

Data Analyst, LXT

Toronto, Canada

Frequently Asked Questions

1. What is Mind2i?

Mind2i is a Bangalore, India based company that provides Live Courses, Corporate Training and Live Consultancy Services to the respective candidates who want to build their career in Python, AI, DataScience, Machine Learning, Deep Learning, NLP, Computer vision and etc.

2. Who are eligible to join Mind2i Live-Courses or Workshops?

Freshers, SOftware-Engineers or any Job-Holders, Graduates with any specialization, Students in middle if their graduation and those who wants in Internship opportunities could consider to join Mind2i.

3. When the Live-Classes will start?

Whenever the new batch starts, it will published in the website and also advertise in variuos social media platforms. When you give details from Mind2i website they will give the information.

4. How the Live-Classes will conduct?

The Live-Classes are conducted through online, at a stipulated time as mentioned from Monday to Friday

5. In what way can I seek solutions for my doubts ?

There will be ticketing system to raise doubts based on that after every class we provide time-slot to raise the doubts.

6. Iam confused in selecting the course?

There was "Apply For Counselling" button in the home page of Mind2i. Once you apply it, our experts or mentors will approach you and give detailed Counselling to understand and make easy to select the course.

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