DATA SCIENCE Education Courses Professional Sessions

We offer DATA SCIENCE and certification in delhi for DATA SCIENCE and information Technology aspirants. Since Decade, we have been in the Information Technology. You can learn more about DATA SCIENCE, Techniques, and Tools to choose a better career path.

Learn DATA SCIENCE after the 10th from the best 5 mentorship in town, providing a senseful Course to understand the fundamentals of DATA SCIENCE in a better way. We possess a Dreamtime of a better understanding of the sincere DATA SCIENCE for the current and prospective students at Rohini Sector-6 Campus Buddy Institutes.

Data Science Syllabus

Want to know about the syllabus of Data Science? The Data Science course syllabus comprises three main components, i.e. Big Data, Machine Learning and Modelling in Data Science. Across these three main components, the subjects cover varied areas of this sought-after discipline. Here is the complete Data Science Syllabus:

MODULES (TOTAL 8)

• Python
• SQL (With MySQL)
• NOSQL (With MongoDB)
• Statistics
• Power BI
• Adv. Excel & VBA
• Machine Learning
• Deep Learning & Neural Networks (AI)

PYTHON

1. Introduction to Python

        ◦ Why Python

        ◦ Application Implementation

        ◦ Python Implementations

        ◦ Python Versions

        ◦ Installing Interpreter Architecture

    2. Writing and Executing First Python Program

        ◦ Using Interactive Mode

        ◦ Using Script Mode

        ◦ Understanding print function

        ◦ How to compile python explicitly

    3. Python Language Fundamentals

        ◦ Character Set

        ◦ Keywords

        ◦ Comments

        ◦ Variables

        ◦ Literals

        ◦ Operators

        ◦ Reading input form console

        ◦ Type conversion

    4. Python Conditional Statements

        ◦ If Statement

        ◦ If else Statement

        ◦ If elif Statement

        ◦ If elif else Statement

        ◦ Nested If Statement

    5. Looping Statements

        ◦ While Loop

        ◦ For Loop

        ◦ Nested Loop

        ◦ Pass, Break and Continue keywords

    6. Standard Data Types

        ◦ int , float , comples

        ◦ bool , Nonetype

        ◦ str , list , tuple

        ◦ dict , set , frozenset

    7. String Handling

        ◦ What is string

        ◦ String representations

        ◦ Unicode string

        ◦ String functions, Methods

        ◦ String Repetition and concatenation

        ◦ String Indexing and Slicing

        ◦ String formatting

    8. Python List

        ◦ Creating and Accessing Lists

        ◦ Indexing and Slicing Lists

        ◦ List Methods

        ◦ Nested List

        ◦ List Comprehension

    9. Python Tuple

        ◦ Creating Tuple

        ◦ Accessing Tuple

        ◦ Immutability of Tuple

    10. Python Set

        ◦ How to create a set

        ◦ Iteration over Sets

        ◦ Python Set methods

        ◦ Python Frozen set

    11. Python Dictionary

        ◦ Creating a Dictionary

        ◦ Dictionary Methods

        ◦ Accessing values form Dictionary

        ◦ Updating Dictionary

        ◦ Iterating Dictionary

        ◦ Dictionary Comprehension

    12. Python Functions

        ◦ Defining a Function

        ◦ Calling a Function

        ◦ Types of Function

        ◦ Function v/s Method

        ◦ Function Arguments

        ◦ Function return Statement

        ◦ Nested Function

        ◦ Function ad argument

        ◦ Decorator function

        ◦ Closure

        ◦ map(),filter(), reduce() ,any()function

        ◦ Anonymous

    13. Modules & Packages

        ◦ Why Modules

        ◦ Script v/s Module

        ◦ Importing Module

        ◦ Standard & Third Party Modules

        ◦ Why Packages

        ◦ Understanding pip utility

    14. File I/O

        ◦ Introduction to file handling

        ◦ File modes

        ◦ Function and methods related to file handling

        ◦ Understanding with block

    15. Object Oriented Programming

        ◦ Procedural v/s Objects oriented programming

        ◦ OOP principles

        ◦ Defining a class & objects creation

        ◦ Inheritance

        ◦ Encapsulation

        ◦ Abstraction

        ◦ Garbage collection

        ◦ Iterator & generator

    16. Exception Handling

        ◦ Difference Between syntax errors and exceptions

        ◦ Keywords used in exception handling

        ◦ Types of Except Blocks

        ◦ User defined Exceptions

    17. GUI Programming

        ◦ Introduction to Tkinter Programming

        ◦ Tkinter Widgets

        ◦ Layout managers

        ◦ Event handling

        ◦ Displaying image

    18. Multi-Threading Programming

        ◦ Multi-processing v/s Multi-threading

        ◦ Need of threads

        ◦ Creating child threads

        ◦ Functions /methods related to threads

        ◦ Thread synchronization and locking

    19. Regular Expressions (Regex)

        ◦ Need of regular Expressions

        ◦ Re module

        ◦ Function/Methods related to regex

        ◦ Meta Characters & Special Sequences

SQL

1. Introduction to Database

        ◦ Database Concepts

        ◦ What is Database Package?

        ◦ Understanding Data Storage

        ◦ Relational Database (RDBMS) Concept

    2. SQL (Structured Query Language)

        ◦ SQL Basics

        ◦ DML, DDL & DQL

        ◦ DDL: Create,Alter,Drop

        ◦ SQL Constraints:

        ◦ DML: Insert, Update, Delete and Merge

        ◦ DQL: Select

        ◦ Select Distinct

        ◦ SQL Where

        ◦ SQL Operators

        ◦ SQL Like

        ◦ SQL order by

        ◦ SQL Aliases

        ◦ SQL Views

        ◦ SQL Joins

        ◦ Full (OUTER) Join

    3. MySQL Functions

        ◦ String Functions

        ◦ Numeric Functions

        ◦ Date Functions

STATISTICS AND ANALYSIS

 1. Introduction to Statistics

        ◦ Sample or Population

        ◦ Measures of Central Tendency

        ◦ Data Distributions

    2. Hypothesis Testing

        ◦ Normality Test

        ◦ Central Limit Theorem

        ◦ Mean test

        ◦ Chi Square test

        ◦ Correlation and Covariance

    3. Numpy Package

        ◦ Difference between list and numpy array

        ◦ Vector and Matrix operations

        ◦ Array indexing and slicing

    4. Pandas Package

        ◦ Introduction to pandas

        ◦ How to load datasets

        ◦ Accessing data form Data Frame

        ◦ Exploratory Data Analysis (EDA)

        ◦ Data Manipulation & Cleaning

        ◦ Categorical Data Encoding

        ◦ Handling Date and Time

        ◦ Data Visualization using matplotlib and seaborn packages

POWER BI

1. Introduction To POWER BI

        ◦ Introduction to Business Intelligence (Bi)

        ◦ Various BI tools

        ◦ Introduction to Power BI

        ◦ Why Power BI

        ◦ Power BI Components

        ◦ Introduction of Power BI Desktop

    2. Data Visualization

        ◦ Understanding Power View and Power map

        ◦ Data visualization techniques

        ◦ Page layout & formatting

        ◦ Power BI Desktop visualization

        ◦ Formatting and customizing visuals

        ◦ Column chart, pie chart, Donut chart,

        ◦ Scatter chart, funnel chart

        ◦ Include & Exclude

        ◦ Geographical data visualization using Maps

        ◦ Drill down

        ◦ Page navigations

        ◦ Bookmarks

        ◦ Selection pane to show/hide visuals

        ◦ Comparing volume and value-based analytics

        ◦ Combinations chart (dual axis charts)

        ◦ Filter pane

        ◦ Slicers

        ◦ Use of hierarchies in drill down analysis

        ◦ Sync slicers

        ◦ Tooltips & custom tooltips

        ◦ Tables & matrix

        ◦ Conditional formatting on visuals

 

    3. POWER BI Service, Publishing & Sharing

        ◦ Introduction to Power BI service

        ◦ Introduction of workspaces

        ◦ Dashboard

        ◦ Creating & configuring dashboards

        ◦ Dashboards theme

        ◦ Reports vs Dashboards

        ◦ Sharing reports & dashboards

    4. Data Transformation – Shaping & Combining Data

        ◦ Shaping data using Power Query Editor

        ◦ Formatting data

        ◦ Transformation of data

        ◦ Understanding of data types

        ◦ Naming conventions & best practices to consider

        ◦ Working with Parameters

        ◦ Merge Query

        ◦ Append Query

        ◦ Group by of data (aggregation of data)

        ◦ Duplicate & reference tables

        ◦ Fill

        ◦ Pivot & Un-pivot of data

        ◦ Custom columns

        ◦ Conditional columns

        ◦ Replace data form the tables

        ◦ Split columns values

        ◦ Move columns & sorting of data

        ◦ Detect data type, count rows & reverse rows

        ◦ Promote rows as column headers

        ◦ Hierarchies in Power BI

 

    5. Data Modeling & Dax

        ◦ Introduction of relationships

        ◦ Creating relationships

        ◦ Cardinality

        ◦ Cross filter direction

        ◦ Use of inactive relationships

        ◦ Introduction of DAX

        ◦ Why DAX is used

        ◦ DAX syntax

        ◦ DAX functions

        ◦ Context in DAX

        ◦ Calculated columns using DAX

        ◦ Measures using DAX

        ◦ Calculated tables using DAX

        ◦ Learning about table, information, logical, text, iterator

        ◦ Time Intelligence functions (YTD, QTD, MTD)

        ◦ Cumulative values, calculated tables, and ranking and rank

        ◦ Date and time functions

Machine Learning

1. Introduction to Machine Learning

        ◦ Traditional v/s Machine Learning Programming

        ◦ Real life examples based on ML

        ◦ Steps of ML Programming

        ◦ Data Preprocessing revised

        ◦ Terminology related to ML

    2. Supervised Learning

        ◦ Classification

        ◦ Regression

    3. Unsupervised Learning

        ◦ Clustering

    4. KNN Classification

        ◦ Math behind KNN

        ◦ KNN implementation

        ◦ Understanding hyper parameters

    5. Performance Metrics

        ◦ Confusion Matrix

        ◦ Accuracy Score

        ◦ Recall & Precision

        ◦ F-1 Score

        ◦ R2 score

    6. Regression

        ◦ Math behind Regression

        ◦ Simple Linear Regression

        ◦ Multiple Linear regression

        ◦ Polynomial regression

        ◦ Boston price prediction

        ◦ Cost or Loss functions

        ◦ Regularization

    7. Logistic Regression for classification

        ◦ Theory of Logistic regression

        ◦ Binary and Multiclass classification

        ◦ Implementing titanic dataset

        ◦ Implementing iris dataset

        ◦ Sigmoid and software functions

    8. Support Vector Machines

        ◦ Theory of SVM

        ◦ SVM Implementation

        ◦ Kernel, gamma, alpha

    9. Decision tree Classification

        ◦ Theory of decision tree

        ◦ Node splitting

        ◦ Implementation with iris dataset

        ◦ Visualizing tree

    10. Ensemble Learning

        ◦ Random Forest

        ◦ Bagging and Boosting

        ◦ Voting Classifier

    11. Model Selection Techniques

        ◦ Cross Validation

        ◦ Grid and Random Search for hyper parameter tuning

    12. Recommendation System

        ◦ Content based technique

        ◦ Collaborative filtering technique

        ◦ Evaluating similarity based on correlation

        ◦ Classification-based recommendations

    13. Clustering

        ◦ K-means Clustering

        ◦ Hierarchical Clustering

        ◦ Elbow technique

        ◦ Silhouette coefficient

        ◦ Dendogram

    14. Text Analysis

        ◦ Install NLTK

        ◦ Tokenize words

        ◦ Tokenizing sentences

        ◦ Stop words customization

        ◦ Stemming and Lemmatization

        ◦ Feature Extraction

        ◦ Sentiment Analysis

        ◦ Count Vectorizer

        ◦ TfidfVectorizer

        ◦ Naive Bayes Algorithms

    15. Dimensionality Reduction

        ◦ Principal Component Analysis(PCA)

    16. Open CV

        ◦ Reading images

        ◦ Understanding Gray Scale Image

        ◦ Resizing image

        ◦ Understanding Haar Classifiers

        ◦ Face , eyes classification

        ◦ How to use webcam in open cv

        ◦ Building image data set

Deep Learning AnD Neural Network

1. Introduction To Artificial Neural Network

        ◦ What is Artificial Neural Network (ANN)?

        ◦ How Neural Network Works?

        ◦ Perceptron

        ◦ Multilayer Perceptron

        ◦ Feed Forward

        ◦ Back propagation

    2. Introduction To Deep Learning

        ◦ What is Deep Learning?

        ◦ Deep Learning Packages

        ◦ Deep Learning Applications

        ◦ Building Deep Learning Environment

    3. Tensor Flow Basics

        ◦ What is Tensorflow?

        ◦ Tensorflow 1.x V/S Tensorflow 2.x

        ◦ Variables, Constants

        ◦ Scalar, Vector, Matrix

        ◦ Operations using tensorflow

        ◦ Difference between tensorflow and numpy operations

        ◦ Computational Graph

    4. Optimizers

        ◦ What does optimizers do?

        ◦ Gradient Descent (full batch and min batch)

        ◦ Stochastic Gradient Descent

        ◦ Learning rate , epoch

    5. Activation Functions

        ◦ What does Activation Functions do?

        ◦ Sigmoid Function,

        ◦ Hyperbolic Tangent Function (tanh)

        ◦ ReLU –Rectified Linear Unit

        ◦ Softmax Function

        ◦ Vanishing Gradient Problem

    6. Building Artificial Neural Network

        ◦ Using scikit implementation

        ◦ Using Tensorflow

        ◦ Understanding MNIST Dataset

        ◦ Initializing weights and biases

        ◦ Gradient Tape

        ◦ Defining loss/cost Function

        ◦ Train the Neural Network

        ◦ Minimizing the loss by adjusting weights and biases

    7. Modern Deep Learning Optimizers and Regularization

        ◦ SGD with Momentum

        ◦ RMSprop

        ◦ AdaGrad

        ◦ Adam

        ◦ Dropout Layers and Regularization

        ◦ Batch Normalization

    8. Building Deep Neural Network Using Keras

        ◦ What is Keras?

        ◦ Keras Fundamental For Deep Learning

        ◦ Keras Sequential Model and Functional API

        ◦ Solve a Linear Regression and Classification Problem with Example

        ◦ Saving and Loading a Keras Model

    9. Convolutional Neural Networks (CNNs)

        ◦ Introduction to CNN

        ◦ CNN Architecture

        ◦ Convolutional Operations

        ◦ Pooling , Stride and Padding Operations

        ◦ Data Augmentation

        ◦ Building ,Training and Evaluating First CNN Model

        ◦ Model Performance Optimization

        ◦ Auto encoders for CNN

        ◦ Transfer Learning and Object Detection Using Pre-trained CNN Models

    10. Word Embedding

        ◦ What is Word Embedding?

        ◦ Word2Vec Embedding

        ◦ Keras Embedding Layers

        ◦ Visualize Word Embedding

        ◦ Google Word2Vec Embedding

        ◦ GloVe Embedding

    11. Recurrent Neural Networks (RNNs)

        ◦ Introduction to RNN

        ◦ RNN Architecture

        ◦ Types of RNN

        ◦ Implementing basic RNN in tensorflow

        ◦ Need for LSTM and GRU

        ◦ Deep RNN/LSTM/GRU

        ◦ Text Classification Using LSTM

        ◦ Prediction for Time Series problem

        ◦ Bidirectional RNN/LSTM

        ◦ Seq-2-Seq Modeling

        ◦ Encoder-Decoder Model

        ◦ Attention Mechanism

    12. Generative Adversarial Networks (GANs)

        ◦ Introduction to GAN

        ◦ Generator

        ◦ Discriminator

        ◦ Types of GAN

        ◦ Implementing GAN using Neural Network

    13. Speech Recognition APIs

        ◦ Text To Speech

        ◦ Speech To Text

        ◦ Automate task using voice

        ◦ Voice Search on Web

    14. Integration of ChatGPT API with Python

        ◦ Introduction to ChatGPT

        ◦ Understanding openai library

        ◦ Registering for an API key

        ◦ API documentation and resources

        ◦ Type of ChatGPT Models

        ◦ Generating Images from ChatGPT API

        ◦ Image Captioning using ChatGPT API

        ◦ Building a Chatbot with ChatGPT API and Python

    15. Projects(Any Five)

        ◦ Stock Price Prediction Using LSTM

        ◦ Object Detection

        ◦ Attendance System Using Face Recognition

        ◦ Facial Expression and Age Prediction

        ◦ Chabot Application

        ◦ Neural Machine Translation

        ◦ Hand Written Digits& Letters Prediction

        ◦ Number Plate Recognition

        ◦ Gender Classification

        ◦ My Assistant for Desktop

        ◦ Suspect Detection using CCTV

        ◦ Hardware operations using gesture detection

        ◦ Cat v/s Dog Image Classification

Advanced Excel

Advanced Excel Course – Overview of the Basics of Excel

        ◦ Customizing common options in Excel

        ◦ Absolute and relative cells

        ◦ Protecting and un-protecting worksheets and cells

    1. Working with Functions

        ◦ Writing conditional expressions (using IF)

        ◦ Using logical functions (AND, OR, NOT)

        ◦ Using lookup and reference functions (VLOOKUP, HLOOKUP, MATCH, INDEX)

        ◦ VLOOKUP with Exact Match, Approximate Match

        ◦ Nested VLOOKUP with Exact Match

        ◦ VLOOKUP with Tables, Dynamic Ranges

        ◦ Nested VLOOKUP with Exact Match

        ◦ Using VLOOKUP to consolidate Data from Multiple Sheets

    2. Advanced Excel Course- Data Validations

        ◦ Specifying a valid range of values for a cell

        ◦ Specifying a list of valid values for a cell

        ◦ Specifying custom validations based on formula for a cell

    3. Advanced Excel Course- Working with Templates

        ◦ Designing the structure of a template

        ◦ Using templates for standardization of worksheets

    4. Advanced Excel Course- Sorting and Filtering Data

        ◦ Sorting tables

        ◦ Using multiple-level sorting

        ◦ Using custom sorting

        ◦ Filtering data for selected view (AutoFilter)

        ◦ Using advanced filter options

    5. Advanced Excel Course- Working with Report s

        ◦ Creating subtotals

        ◦ Multiple-level subtotals

        ◦ Creating Pivot tables

        ◦ Formatting and customizing Pivot tables

        ◦ Using advanced options of Pivot tables

        ◦ Pivot charts

        ◦ Consolidating data from multiple sheets and files using Pivot tables

        ◦ Using external data sources

        ◦ Using data consolidation feature to consolidate data

        ◦ Show Value As ( % of Row, % of Column, Running Total, Compare with Specific Field)

        ◦ Viewing Subtotal under Pivot

        ◦ Creating Slicers ( Version 2010 & Above)

    6. Advanced Excel Course- More Functions

        ◦ Date and time functions

        ◦ Text functions

        ◦ Database functions

        ◦ Power Functions (CountIf, CountIFS, SumIF, SumIfS)

    7. Advanced Excel Course- Formatting

        ◦ Using auto formatting option for worksheets

        ◦ Using conditional formatting option for rows, columns and cells

    8. Advanced Excel Course- Macros

        ◦ Relative & Absolute Macros

        ◦ Editing Macro’s

    9. Advanced Excel Course- WhatIf Analysis

        ◦ Goal Seek

        ◦ Data Tables

        ◦ Scenario Manager

    10. Advanced Excel Course- Charts

        ◦ Using Charts

        ◦ Formatting Charts

        ◦ Using 3D Graphs

        ◦ Using Bar and Line Chart together

        ◦ Using Secondary Axis in Graphs

        ◦ Sharing Charts with PowerPoint / MS Word, Dynamically

        ◦ (Data Modified in Excel, Chart would automatically get updated)

    11. Advanced Excel Course- New Features Of Excel

        ◦ Sparkline, Inline Charts, data Charts

        ◦ Overview of all the new features

    12. Advanced Excel Course- Final Assignment

        ◦ The Final Assignment would test contains questions to be solved at the end of the Course

VBA (VISUAL BASIC FOR APPLICATION) & MACROS

    1. Create a Macro:

        ◦ Swap Values, Run Code from a Module, Macro Recorder, Use Relative References,

        ◦ FormulaR1C1, Add a Macro to the Toolbar, Macro Security, Protect Macro.

    2. MsgBox:

        ◦ MsgBox Function, Input Box Function.

    3. Workbook and Worksheet Object:

        ◦ Path and Full Name, Close and Open, Loop through Books and Sheets, Sales Calculator, Files in a

        ◦ Directory, Import Sheets, Programming Charts.

    4. Range Object:

        ◦ Current Region, Dynamic Range, Resize, Entire Rows and Columns, Offset, From Active Cell to

        ◦ Last Entry, Union and Intersect, Test a Selection, Possible Football Matches, Font, Background

        ◦ Colors, Areas Collection, Compare Ranges.

    5. Variables:

        ◦ Option Explicit, Variable Scope, Life of Variables.

    6. If Then Statement:

        ◦ Logical Operators, Select Case, Tax Rates, Mod Operator, Prime Number Checker, Find Second

        ◦ Highest Value, Sum by Color, Delete Blank Cells.

    7. Loop:

        ◦ Loop through Defined Range, Loop through Entire Column, Do Until Loop, Step Keyword, Create a

        ◦ Pattern, Sort Numbers, Randomly Sort Data, Remove Duplicates, Complex Calculations, Knapsack

        ◦ Problem.

    8. Macro Errors:

        ◦ Debugging, Error Handling, Err Object, Interrupt a Macro, Macro Comments.

    9. String Manipulation:

        ◦ Separate Strings, Reverse Strings, Convert to Proper Case, Count Words.

    10. Date and Time:

        ◦ Compare Dates and Times, DateDif Function, Weekdays, Delay a Macro, Year Occurrences, Tasks

        ◦ On Schedule, Sort Birthdays.

    11. Events:

        ◦ Before DoubleClick Event, Highlight Active Cell, Create a Footer Before Printing, Bills and Coins,

        ◦ Rolling Average Table

    12. Array:

        ◦ Dynamic Array, Array Function, Month Names, Size of an Array.

    13. Function and Sub:

        ◦ User Defined Function, Custom Average Function, Volatile Functions, ByRef and ByVal.

    14. Application Object:

        ◦ Status Bar, Read Data from Text File, Write Data to Text File.

    15. ActiveX Controls:

        ◦ Text Box, List Box, Combo Box, Check Box, Option Buttons, Spin Button, Loan Calculator.

    16. User form:

        ◦ User form and Ranges, Currency Converter, Progress Indicator, Multiple List Box Selections,

        ◦ Multicolumn Combo Box, Dependent Combo Boxes, Loop through Controls, Controls Collection,

        ◦ User form with Multiple Pages, Interactive User form