Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom, Campus Based/Physical Classroom | Video and Text Based |
Course Overview
Realizing the increase in data and demand due to digitalization in the Data Science space, the Data Science Course Training in Delhi focuses on enhancing the skills and abilities of students who are looking to become successful Data Science Professionals.
This Data Science Course Training in Delhi offers both virtual classroom sessions and self-paced training where students can choose their preferred style of learning. After the Data Science training course in Delhi is completed, students will gain expertise which is equal to a Data Science professional. Data Science Course Training in Delhi syllabus each and every concept of Data Science like Data Cleansing, Data Transformation, Data Collection, Data Mining, Data Extraction, Data Visualization, and so on. Also, it teaches several skills and techniques like Python programming, Machine Learning, Hypothesis Testing, Neural Networks, Regression Modelling, Microsoft Azure, R-programming, Statistical Analysis, and so on.
Excel R has the best teaching faculty that ensures a masterclass learning experience for students. All the trainers are Data Scientists who are extremely skilled with over 15 years of experience. Most of the faculty are from prestigious institutions like IITs, IIMs, BITS Pilani, and so on. Students will get to work on various project works, assignments, and live-projects which will strengthen their technical skills and gain in-field experience. This Data Science Course Training in Delhi classes also provides an option for students to access e-learning such that students can watch the recorded sessions to recap and revise the topics or missed sessions.
Given the fact that Excel R is the best institution for Data Science certification Course Training in Delhi, it is highly-advantageous for students who want to start their career in the field of Data Science.
The Highlights
- Instructor-led training
- Live classroom virtual sessions
- Self-paced learning
- Lifetime access
- Dual certification by SGIT and Excel R
- 6 months duration
- In association with Steinbeis University
- Placement assistance
Programme Offerings
- assignments
- Peer support
- Live Projects
- E-learning
- Webinars
- Self-paced learning
- instructor-led training
- Post training support
- Interview preparation sessions
- Amount in INR Live virtual classroom.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | FutureSkillsExcelR SolutionsSteinbeis Global Institute, Tubingen |
The table for the fee details is as follows:
| Fees components | Amount |
| Without IITM Pravartak Certification | Rs. 54999 |
| With IITM Pravartak Certification | Rs. 75999 |
Eligibility Criteria
Education
The educational qualification for pursuing Data Science Course Training in Delhi is that students should possess technical knowledge of Python, R-programming, and SAS tools and mathematical skills. These are the important skill sets for course takers in order to learn this course.
Certification Qualifying Details
Upon the successful completion of the course, students must take an online exam conducted by SteinbeisUniversity and get a minimum of 60% to get the certificate of completion. It is a dual certificate from Excel R and SteinbeisUniversity which is a sign of excellence in the field of Data Science. Students will enjoy the alumni status of SGIT, SteinbeisUniversity.
What you will learn
There is so much that students will extract from the Data Science Course Training in Delhi such as -
- Many applications, theories, and concepts of Data Science like Data Exploration, Feature Engineering, Building Prediction models, Data Visualization, Data Transformation, Data Integration, Data Mining, Data Cleansing, and so on.
- Various additional skills and tools like Machine Learning, Neural Networks, Statistical Analysis, Hypothesis Testing, Deep Learning, Tableau, Predictive Modelling, Spark, Programming languages like R programming, Python, R Studio, Text Mining, Predictive Analytics, Natural Language Processing, Hadoop, Regression Modelling and so on to carry out different tasks.
Who it is for
The Data Science Course Training in Delhi is highly suggested for the following individuals such as -
- Freshers with good math, analytical skills, problem-solving and logical reasoning who want to kick start their career in Data Science.
- Qualified professionals in data warehousing, business intelligence, and reporting tools who want to polish their skills.
- Data Scientists, Data Analysts, Research Analysts, Business Analyst Consultants, and so on who want to brush up on their expertise and get many perks like new opportunities, promotions, high salary packages, and so on.
Admission Details
The admission process to enroll in this course is direct and easy. It is advised that students should keep their payment details handy.
Students should follow certain steps to register for the Data Science certification Course Training in Delhi is as follows -
Step 1: Go to the course page on the official website of Excel R: https://www.excelr.com/data-science-course-training-in-delhi
Step 2: Choose your preferred course option between self-paced training and live virtual classroom.
Step 3: Enter coupon code and apply if you have one.
Step 4: Click on ‘Proceed’ and log in to your Excel R account or register if you haven’t yet.
Step 5: Fill in your credentials.
Step 6: Choose your preferred payment option.
Step 7: Fill in your payment details.
Step 8: Click on ‘Make a Payment’ and confirm the payment.
Step 9: After the transaction is completed, you can have access to the course.
The Syllabus
Module 1 - Statistical Analysis
- Data Types
- Measure Of central tendency
- Measures of Dispersion
- Graphical Techniques
- Skewness & Kurtosis
- Box Plot.
- Random Variable
- Probability
- Probability Distribution
- Normal Distribution
- SND
- Expected Value
- Sampling Funnel
- Sampling Variation
- Central Limit Theorem
- Confidence interval
Module 2 - Hypothesis Testing
- Introduction to Hypothesis Testing
- Hypothesis Testing ( 2 proportion test, 2 t sample t test)
- Anova and Chi Square
Module 3 - Linear And Logistic Regression
- Principles of Regression
- Intro to Simple Linear Regression
- Multiple Linear Regression
- Logistic Regression
Module 4 -EDA
- Data Cleaning
- Imputation Techniques
- Data analysis and Visualization
- Scatter Diagram
- Correlation Analysis
- Transformations
- Encoding Methods - OHE, Label Encoders,Outlier detection-Isolation Forest and Calculating the Predictive Power Score (PPS)
Module 5 - Unsupervised ML Algorithms
- Clustering introduction
- Hierarchical clustering
- K Means
- DBSCAN
- PCA
- Association Rules
- Recommender System
- Python Model Deployment
Module 6 - Machine Learning Models
- Regression Tasks / Classification Tasks
- Decision Tree
- KNN
- Support Vector Machines
- Feature Engineering (Tree based methods, RFE,PCA)
- Model Validation Methods (train-test,CV,Shuffle CV, and Accuracy methods)
- Lasso and Ridge Regressions
Module 7 - Neural Network
- ANN
- Optimization Algorithm(Gradient descent)
- Stochastic gradient descent(intro)
- Back Propagation method
- Introduction to CNN
Module 8 - Bagging And Boosting
- Bagging and Random Forest
- Boosting
- XGBM
- LGBM
Module 9 - Text Mining
- introduction to Text Mining
- VSM
- Intro to word embeddings
- Word clouds and Document Similarity using cosine similarity
- Named Entity Recognition
- Text classification using Naive Bayes
- Emotion Mining
Module 10 - Forecasting
- Introduction to Time Series
- Level
- Trend and Seasonality
- Strategy
- Scatter plot
- Lag plot
- ACF
- Principles of Visualization
- Naive forecasts
- Forecasting Error and it metrics
- Model Based Approaches
- AR Model for errors
- Data driven approaches
- MA
- Exponential Smoothing
- ARIMA
Module 11 - Introduction
- Python Introduction- Programing Cycle of Python,PythonIDE and Jupyter Notebook
Module 12 - Variables
- Variables
- DataType
Module 13 - Code Practice Platform
- Github
- HackerRank
- CodeWars and Sanfoundry Account Creation Number
- String
- List
- Tuple
- Dictionary
Module 14 - Operators, Loops & String
- Operator-Arithmetic
- Comparison
- Decision Making-Loops
- While Loop
- For Loop and Nested Loop
- Number Type Conversion-int(), long().Float()
- Strings-EscapeChar
- String Special Operator
- String Formatting Operator
Module 15: List, Tuples, And Dictionary
- Python List
- Accessing values in list
- Delete list elements
- Indexing, Slicing & Matrices
- Tuples
- Accessing values in Tuples
- Delete Tuples elements
- Indexing
- Slicing & Matrices
- Dictionary
- Accessing Values from Dictionary
- Deleting and Updating Elements in Dict
- Properties of Dist
- Built-In Dist Functions & Methods
- Dict Comprehension
Module 16: Function & Modules
- Function
- Define Function
- Calling Function
- Pass by Reference as Value
- Function Arguments
- Anonymous Functions
- Return Statements
- Scope of Variables
- Local & Global
- Decorators and Recursion
- Import Statements
- Locating Modules
- Current Directory
- Python path
- Dir() Function
- Global and Location Functions & Reload() Functions
- Sys Module and Subprocess Module
- Packages in Python
Module 17: Files & Directories
- Files in Python
- Reading Keyboard Input
- Input Function
- Opening and Closing Files
- Syntax and List of Modes
- Files Object Attribute Open,Close.
- Reading and Writing Files
- File Position Directories Mkdir Method
- Chdir() Method
- Getcwd Method
- Rmdir
Module 18 - Exception Handling
- Exception Handling
- List of Exceptions
- TryandException
Module 19 - OOP
- OOP Concepts, Class, Objects, Inheritance, Overriding Methods like __init__, Overloading Operators, Data Hiding
Module 20 - Regular Expressions
- Match Function
- Search Function
- Matching Vs Searching
- Regular Exp Modifiers and Patterns
Module 21 - SQLite And MySQL
- Database Connectivity
- Methods
- MySQL
- Oracle
- How to Install MySQL
- DB Connection
Module 22 - Tableau Products And Usage
- What is Tableau ?
- What is Data Visualization ?
- Tableau Products
- Tableau Desktop Variations
- Tableau File Extensions
- Data Types
- Dimensions
- Measures
- Aggregation concept
- Tableau Desktop Installation
- Data Source Overview
- Live Vs Extract
Module 23 - Charts On Tableau
- Bar Chart
- Pi-Chart
- Heat Maps
- Histogram
- Maps
- Scatterplot
- Donut Chart
- Waterfall Chart etc..
- Dual axis
- Blended axis
Module 24 - Filters And Calculations
- Dimension Filter
- Measure Filter
- Data Source Filter
- Extract Filter
- Context Filter
- Quick Filter
- Basic Calculations
- Table Calculations
- Quick Table Calculations
- LOD's
- KPI's
Module 25 - Data Combining Techniques
- Joins
- Relationship
- Data Blending
- Union
Module 26 - Grouping The Data
- Hierarchy
- Group
- Sets
- Parameters
Module 27 - Analytics & Dashboard
- Reference Lines
- Trend Line
- Forecasting
- Clustering
- Dashboard Objects
- Dashboard Actions
- Tableau Public website
Module 28 - Introduction To Mysql
- Introduction to Databases
- Introduction to RDBMS
- Different types of RDBMS
- Software Installation(MySQL Workbench)
Module 29 - SQL Commands
- Data Definition language
- Data Manipulation Language
- Data Query Language
- Transactional Control Language
- Data Control Language
Module 30 - DQL Operators
- SELECT
- LIMIT
- DISTINCT
- WHERE
- AND
- OR
- IN
- NOT IN
- BETWEEN
- EXIST
- ISNULL
- IS NOT NULL
- WILD CARDS
- ORDER BY
- GROUP BY
- HAVING
Module 31 - Functions
- COUNT
- SUM
- AVG
- MIN
- MAX
- COUNT
- String Functions
- Date & Time Function
Module 32 - Constraints
- NOT NULL
- UNIQUE
- CHECK
- DEFAULT
- ENUM
- Primary key
- Foreign Key (Both at column level and table level)
Module 33 - Joins
- Inner
- Left
- Right
- Cross
- Self Joins
- Full outer join
Module 34 - SQL Concepts
- Index
- View
- Sub-query
- Window Functions
- Stored Procedures
- Exception Handling
- Loops
- Cursor
- Triggers
Module 35 - Introduction ToNeural Network & DeepLearning
- Introduction
- DeepLearningImportance[Strength & Limitation]
- SP | MLP Neural Network Overview
- Neural Network Representation Activation Function
- Loss Function Importance of Non-LinearActivation Function
- Gradient Descent for NeuralNetwork
Module 36 - Parameter & Hyperparameter
- Train
- Test & Validation Set
- Vanishing &ExplodingGradient
- Dropout Regularization
- OptimizationAlgo
- LearningRate
- Tuning
- Softmax
Module 37 - CNN
- CNN
- Deep Convolution Model
- Detection Algorithm
- CNN FaceRecognition
Module 38 - RNN
- RNN
- LSTM
- BiDirectionalLSTM
Module 39 - Hadoop
- Introduction to BigData, Challenges in Big Data and Workarounds| Introduction to Hadoop and Its Components|HadoopComponents andHands-On|Understand the Map Reduce and ItsDrawbacks
Module 40 - Spark & Data Bricks
- Introduction to Spark and DataBricks|Spark Components, Spark MLlib Spark &DataBricks andHands-On One ML Model in Spark
Module 41 - Azure
- Cloud Computing
- Azure Cloud Platform
- Cloud Applications
- Cloud Services
- Open AI Studio
Module 42 - R And RStudio
- Data Structures & Operators in R|Conditional Statement|Decision Making|Loops|Strings|Functions|How to Import Data set in R| Programming Statistical Graphics
Module 43 - ChatGPT
- Introduction to ChatGPT and AI
- Types of AI and ChatGPT architecture
- ChatGPT Functionalities and Applications
- ChatGPT Prompt Engineering