Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual 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 68999Project works
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Steinbeis Global Institute, TubingenExcelR SolutionsFutureSkills |
The fee details of the Data Science Course Training in Delhi is given below:
- Students will have options to choose between self-paced learning and live virtual classrooms.
- Self-paced learning is priced at Rs 68999 .
- There is a facility of monthly EMI at a zero interest rate on all major credit cards.
The table for the fee details is as follows:
Fee structure | Amount in INR |
Live virtual classroom | 68999 |
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
- Recap of Demo
- Introduction to Types of Analytics
- Project life cycle
- An introduction to our E-learning platform
- Data Types
- Measure Of central tendency
- Measures of Dispersion
- Graphical Techniques
- Skewness & Kurtosis
- Box Plot
- R
- R Studio
- Descriptive Stats in R
- Python (Installation and basic commands) and Libraries
- Jupyter notebook
- Set up Github
- Descriptive Stats in Python
- Pandas and Matplotlib / Seaborn
- Random Variable
- Probability
- Probility Distribution
- Normal Distribution
- SND
- Expected Value
- Sampling Funnel
- Sampling Variation
- CLT
- Confidence interval
- Assignments Session-1 (1 hr)
- Introduction to Hypothesis Testing
- Hypothesis Testing with examples
- 2 proportion test
- 2 sample t test
- Anova and Chisquare case studies
- Visualization
- Data Cleaning
- Imputation Techniques
- Scatter Plot
- Correlation analysis
- Transformations
- Normalization and Standardization
- Principles of Regression
- Introduction to Simple Linear Regression
- Multiple Linear Regression
- Multiple Logistic Regression
- Confusion matrix
- False Positive, False Negative
- True Positive, True Negative
- Sensitivity, Recall, Specificity, F1 score
- Receiver operating characteristics curve (ROC curve)
- R shiny
- Streamlit
- Supervised vs Unsupervised learning
- Data Mining Process
- Visualization of clustering algorithm using Dendrogram
Hierarchical Clustering / Agglomerative Clustering
- Measure of distance
- Numeric - Euclidean, Manhattan, Mahalanobis
- Categorical - Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient
- Mixed - Gower’s General Dissimilarity Coefficient
- Types of Linkages
- Single Linkage / Nearest Neighbour
- Complete Linkage / Farthest Neighbour
- Average Linkage
- Centroid Linkage
K-Means
- Non-Hierarchial
- Measurement metrics of clustering - Within Sum of Squares, Between Sum of Squares, Total Sum of Squares
- Choosing the ideal K value using Scree plot / Elbow Curve
DBSCAN
- A general intuition for DBSCAN
- Different parameters in DBSCAN
- Metrics used to evaluate the performance of the model
- Pro's and Con's of DBSCAN
- PCA and tSNE
- Why dimension reduction
- Advantages of PCA
- Calculation of PCA weights
- 2D Visualization using Principal components
- Basics of Matrix algebra
- What is Market Basket / Affinity Analysis
- Measure of association
- Support
- Confidence
- Lift Ratio
- Apriori Algorithm
- User-based collaborative filtering
- The measure of distance/similarity between users
- Driver for recommendation
- Computation reduction techniques
- Search based methods / Item to item collaborative filtering
- The vulnerability of recommender systems
- Workflow from data to deployment
- Data nuances
- Mindsets of modelling
- Elements of Classification Tree - Root node, Child Node, Leaf Node, etc.
- Greedy algorithm
- Measure of Entropy
- Attribute selection using Information Gain
- Implementation of a Decision tree using C5.0 and Sklearn libraries
- Encoding Methods
- OHE
- Label Encoders
- Outlier detection-Isolation Fores
- Predictive power Score
- Recurcive Feature Elimination
- PCA
- Splitting data into train and test
- Methods of cross-validation
- Accuracy methods
- Bagging
- Boosting
- Random Forest
- XGBM
- LGBM
- Deciding the K value
- Building a KNN model by splitting the data
- Understanding the various generalization and regulation techniques to avoid overfitting and underfitting
- Kernel tricks
- Lasso Regression
- Ridge Regression
- Artificial Neural Network
- Biological Neuron vs Artificial Neuron
- ANN structure
- Activation function
- Network Topology
- Classification Hyperplanes
- Best fit “boundary”
- Gradient Descent
- Stochastic Gradient Descent Intro
- Back Propagation
- Introduction to concepts of CNN
- Sources of data
- Bag of words
- Pre-processing, corpus Document-Term Matrix (DTM) and TDM
- Word Clouds
- Corpus level word clouds
- Sentiment Analysis
- Positive Word clouds
- Negative word clouds
- Unigram, Bigram, Trigram
- Vector space Modelling
- Word embedding
- Document Similarity using Cosine similarity
- Extract Tweets from Twitter
- Extract user reviews of the products from Amazon, Snapdeal and TripAdvisor
- Install Libraries from Shell
- Extraction and text analytics in Python
- Sentiment Extraction
- Lexicons and Emotion Mining
- Probability – Recap
- Bayes Rule
- Naive Bayes Classifier
- Text Classification using Naive Bayes
- Introduction to time series data
- Steps of forecasting
- Components of time series data
- Scatter plot and Time Plot
- Lag Plot
- ACF - Auto-Correlation Function / Correlogram
- Visualization principles
- Naive forecast methods
- Errors in forecast and its metrics
- Model Based approaches
- Linear Model
- Exponential Model
- Quadratic Model
- Additive Seasonality
- Multiplicative Seasonality
- Model-Based approaches
- AR (Auto-Regressive) model for errors
- Random walk
- ARMA (Auto-Regressive Moving Average), Order p and q
- ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
- Data-driven approach to forecasting
- Smoothing techniques
- Moving Average
- Simple Exponential Smoothing
- Holts / Double Exponential Smoothing
- Winters / HoltWinters
- De-seasoning and de-trending
- Forecasting using Python and R
- Concept with a business case
- End to End project Description with deployment using R and Python
Basic Statistics
- Data types Identification and probability
- Expected values, Measures of central tendencies
- Skewness and Kurtosis & Boxplot
- Practice Mean, Median, Variance, Standard Deviation and Graphical representations in R
- Creating Python Objects
- Practice Mean, Median, Variance, Standard Deviation and Graphical representations in Python
- Confidence intervals and distributions
Hypothesis Testing
- Buyer ratio
- Customer Order Form
- Cutlets
- Pantaloons
- Lab TAT
Linear regression
- Prediction of weight based on Calories consumed
- Delivery Time period Vs Sorting time
- Employee Churn rate Vs Salary
- Salary Prediction
R shiny and Flask
- Practice R shiny and Python Flask for Linear Regression assignments
Multiple Linear Regression
- 50 startups case study
- Computer data Case study
- Toyota Corolla
Logistic Regression
- Term deposit case study
- Elections results Case study
Multinomial Regression
- Student Program Case study
Hierarchical Clustering
- Crime data
- Eastwest Airlines
K means Clustering
- Insurance policy
- Crime data
PCA
- Dimension Reduction for Wine data
Network Analytics
- Node Properties practice in R
Association Rulesv
- Association Rules for Book store
- Association Rules for Mobile store
- Association Rules for Retail Transactions
Recommendation Engine
- Recommend Jokes for subscribers
Text mining, Web Extraction
- Extraction of tweets from twitter
- Reviews from ecommerce websites
Text mining
- Sentiment Analysis on extracted data
NLP
- Emotion mining by extracting a speech or novel from web
Naive Bayes
- Spam and Ham classifications
KNN Classifier
- Types of Glass
- Classification of Animals
Decision Tree and Random Forest
- Fraud Check
- Sales prediction of an Organization
XGB and GLM
- Social Networks Ads
Lasso and Ridge Regression
- Practice Lasso and Ridge with multiple Linear Assignments
ANN
- Forest Fires case study
SVM
- Classification of Alphabets
Survival analysis
- Prediction of Patient survival probability
Forecasting model based
- Airlines Forecasting
- Forecasting of sales for a soft drinks case study
Forecasting
- Forecasting of Bike shares
- Forecasting of Solar power consumption
Industry: Aviation
- Predicting the flight delays
Industry: Manufacturing
- Predict impurity in ore
Industry: Oil and Gas
- Predicting the oil price
Industry : Automotive
- Electric Motor Temperature
Industry : Daily Analysis of a product
- "Daily" Twitter Data Analysis for a Product
Industry : E commerce
- Natural Language Processing
- Resume Preparation
- Interview Support
- Introduction to Big Data
- Challenges in Big Data and Workarounds
- Introduction to Hadoop and its Components
- Hadoop components and Hands-on
- Understand the MapReduce (Distributed Computation Framework) and its Drawback
- Introduction to Spark
- Spark Components
- Spark MLlib and Hands-on (one ML model in spark)
Introduction to R Programming
- Introduction to R
- Data Types in R
How To Install R & R Studio
Data Structures in R
- Variable in R
- R-Overview
- Vector
- Matrix
- Array
- List
- Data-Frame
- Operators in R
- arithmetic
- Relational
- Logical
- Assignment
- Miscellaneous
- Conditiional Statement
- Decision Making
- IF Statement
- IF-Else Statement
- Nested IF-Else Statement
- Switch Statement
- Loops
- While Loop
- Repeat Loop
- For Loop
- Strings
- Functions
- User-defined Function
- Calling a Function
- Calling a Function without an Argument
- Calling a Function with an Argument
- Decision Making
Programming Statistical
- Box Plots
- Bar Charts
- Histogram
- Pareto Chart
- Pie Chart
- Line Chart
- Scatterplot
How to Import Dataset in R
- Read CSV Files
- Read Excel Files
- Read SAS Files
- Read STATA Files
- Read SPSS Files
- Read JSON Files
- Read Text Files
R-Packages
- DpLyr
- Hmisc or mise
- Ggplot2
- Caret
- Data Table
How to Integrate R and SQL
How to Get Data From SQL to R
Introduction
- Python Introduction - Programing Cycle of Python
- Python IDE and Jupyter notebook
Variables
- Variables
- Data type
Code Practice Platform
- create , insert , update and delete operation , Handling erros
Operators
- Operator -Arthmatic ,comparison , Assignment ,Logical , Bitwise opeartor
- Decision making - Loops
Loops
- While loop, for loop and nested loop
- Number type conversion - int(), long(). Float ()
- Mathametical functions , Random function , Trigonometric function
Sting
- Strings- Escape char, String special Operator , String formatting Operator
- Build in string methods - center(), count()decode(), encode()
List
- Python List - Accessing values in list, Delete list elements , Indexing slicing & Matrices
- Built in Function - cmp(), len(), min(), max(), list comprehension
Tuples
- Tuples - Accessing values in Tuples, Delete Tuples elements , Indexing slicing & Matrices
- Built in tuples functions - cmp(), len ()
Dictionary
- Dictionary - Accessing values from dictionary, Deleting and updating elements in Dict.
- Properties of Dist. , Built in Dist functions & Methods, Dict comprehension
- Date & time -Time Tuple , calendor module and time module
Function
- Function - Define function , Calling function
- pass by refernece as value , Function arguments , Anonymous functions , return statements
- Scope of variables - local & global , Decorators and recursion
- Map reduce and filter
Modules
- Import statemnts , Locating modules - current directory , Pythonpath
- Dir() function , global and location functions and reload () functions , Sys module and subprocess module
- Packages in Python
Files
- 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.
- Renaming and deleting files
- Pickle and Json
Directories
- mkdir methid, chdir () method , getcwd method , rm dir
Exception Handling
- Exception handling - List of exceptions - Try and exception
- Try- finally clause and user defined exceptions
OOP
- OOP concepts , class , objects , Inheritance
- Overriding methods like _init_, Overloading operators , Data hiding
Regular Expressions
- match function , search function , matching vs searching
- Regular exp modifiers and patterns
SQLite and My SQL
- Data base connectivity
- Methods- MySQL , oracle , how to install MYSQL , DB connection
- create , insert , update and delete operation , Handling erros
Framework
- Introduction to Django framwork , overview , environment
- Apps life cycle , creating views
- Application, Rest API
- Introduction to What is DataBase
- Difference between SQL and NOSQL DB
- How to Install MYSQL and Workbench
- Connecting to DB
- Creating to DB
- What are the Languages inside SQL How to Create Tables inside DB and Inserting the Records
- Select statement and using Queries for seeing your data
- Joining 2 tables
- Where clause usage
- Indexes and views
- Different operations in SQL
- How to Connect to your applications from MYSQL includes R and Python
What is Data Visualization?
- Why Visualization came into Picture?
- Importance of Visualizing Data
- Poor Visualizations Vs. Perfect Visualizations
- Principles of Visualizations
- What is R?
How to integrate Tableau with R?
Tableau Prep
- Tufte’s Graphical Integrity Rule
- Tufte’s Principles for Analytical Design
- Visual Rhetoric
- Goal of Data Visualization
Tableau – Data Visualization Tool
- Introduction to Tableau
- What is Tableau? Different Products and their functioning
- Architecture Of Tableau
- Pivot Tables
- Split Tables
- Hiding
- Rename and Aliases
- Data Interpretation
Tableau User Interface
- Understanding about Data Types and Visual Cues
Basic Chart types
- Text Tables, Highlight Tables, Heat Map
- Pie Chart, Tree Chart
- Bar Charts, Circle Charts
Intermediate Chart
- Time Series Charts
- Time Series Hands-On
- Dual Lines
- Dual Combination
Advanced Charts
- Bullet Chart
- Scatter Plot
- Introduction to Correlation Analysis
- Introduction to Regression Analysis
- Trendlines
- Histograms
- Bin Sizes in Tableau
- Box Plot
- Pareto Chart
- Donut Chart, Word Cloud
- Forecasting ( Predictive Analysis)
Maps in Tableau
- Types of Maps in Tableau
- Polygon Maps
- Connecting with WMS Server
- Custom Geo coding
- Data Layers
- Radial & Lasso Selection
Adding Background Image
- How to get Background Image and highlight the data on it
- Creating Data Extracts
- Filters and their working at different levels
- Usage of Filters on at Extract and Data Source level
- Worksheet level filters
- Context, Dimension Measures Filter
Data Connectivity in-depth understanding
- Joins
- Unions
- Data Blending
- Cross Database Joins
- Sets
- Groups
- Parameters
Creating Calculated Fields
- Logical Functions
- Case-If Function
- ZN Function
- Else-If Function
- Ad-Hoc Calculations
- Quick Table Calculations
- Level of Detail (LoD)
- Fixed LoD
- Include LoD
- Exclude LoD
Responsive Tool Tips
- Dashboards
- Actions at Sheet level and Dashboard level
- Story
Connecting Tableau with Tableau Server
- Publishing our Workbooks in Tableau Server
- Publishing dataset on to Tableau Server
- Setting Permissions on Tableau Server
Connecting Tableau with R
- What is R?
- How to integrate Tableau with R?
- Tableau Prep
Introduction to Neural Network & Deep Learning
- Introduction
- Deep Learning Importance [Strength & Limitation]
- SP | MLP
- Neural Network Overview
- Neural Network Representation
- Activation Function
- Loss Function
- Importance of Non-linear Activation Function
- Gradient Descent for Neural Network
Parameter & Hyper parameter
- Train, Test & Validation Set
- Vanishing & Exploding Gradient
- Dropout
- Regularization
- Optimization algorithm
- Learning Rate
- Tuning
- Softmax
CNN
- CNN
- Deep Convolution Model
- Detection Algorithm
- Face Recognition
RNN
- RNN
- LSTM
- Bi Directional LSTM
Introduction to ChatGPT and AI
- What is ChatGPT?
- The history of ChatGPT
- Applications of ChatGPT
- ChatGPT vs other chatbot platforms
- Industries using ChatGPT
- The benefits and limitations of ChatGPT
- Future developments in ChatGPT technology
- Ethical considerations related to ChatGPT and AI
Types of AI and Chatgpt architecture
- Narrow AI
- Strong AI
- Superintelligence
- Chatgpt architecture
ChatGPT Functionalities and Applications
- How does ChatGPT work?
- ChatGPT Functionalities
- Drafting emails and professional communication
- Automating content creation
- Resume and Cover letter creation
- Research and information gathering
- Brainstorming ideas and creative problem solving
- Best Practices for Using ChatGPT
ChatGPT Prompt Engineering
- What is Prompt Engineering?
- Types of Prompts
- Crafting Effective Prompts
- Using ChatGPT to generate prompt
Steinbeis University, Berlin Frequently Asked Questions (FAQ's)
There isn’t any prerequisite included in this course. But, it is recommended that students must have good command over mathematics, critical thinking, analytical skills, communication skills, Python, R, and SAS tools.
The duration of this Data Science Course Training in Delhi is 6 months such that students will get ample time to learn concepts.
Students must get a minimum of 60% to pass the exam. However, it varies from course to course.
The certificate is a dual certificate provided by Steinbeis University and Excel R. It is a true symbol of excellence in Data Science Space. This certificate has a global recognition that fetches several new opportunities, placements, and so on. Also, students will enjoy the alumni of SGIT.
No, students must pay the Data Science Course Training in Delhi fee in order to pursue this course.
There are two-course options. Live virtual classroom and self-paced learning. Students should choose from these options.
This course offers an e-learning feature where students can access the missed lectures or recap and revise the previous concepts.
Students can avail of monthly EMI on all major credit cards at a zero interest rate.
Yes, students will get job assistance after the completion of the course.
Students must have a good grip on mathematics, problem-solving skills, analytical skills, Python, R, SAS tools, communication skills, and critical thinking skills.