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
Pertaining to the requirement of the extensive knowledge of Data Science, the Data Science Certification Course Training by Excel R. Data science has constantly been the highest in-demand profession and the most sought-after profession as per Harvard. This Data Science Certification Course Training syllabus has been carefully planned to not only cover the basic statistics using R and Python but also most of the advanced topics of Data science Survival analysis through Data Science.
Data science being related to all about mining hidden insights of data by analysing behaviour, inferences, interpretations & trends, this course is designed in such a manner that candidates get to develop their trend interpretation & behavioural analysis skills. Through the perfectly blended Data Science Certification Course method, candidates can avail pre-recorded sessions, classroom and instructor-led online sessions with a single enrolment, which efficiently produces a synergistic impact on learning. Through Data Science Certification Course Training syllabus, text mining to neural networks to regularisation techniques, candidates get a very wide-coverage of the various topics related to data science.
The Highlights
- Access to a huge data science interview repository.
- Assured placement support.
- Lifetime access.
- 2 Real life capstone projects.
- Post-training support.
- SGIT Alumnus Status.
- Both synchronous & asynchronous programmes available.
- Six months of duration
Programme Offerings
- video lectures
- Live Projects
- Graded Assignments.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | ExcelR SolutionsSteinbeis Global Institute, Tubingen |
The Data Science Certification Course Training fee by Excel R is as follows:
| Head | Amount in USD |
| With IITM certificate | $ 1599 |
Eligibility Criteria
Certification Qualifying Details
After completion of the training, a candidate must take an online examination facilitated by the university & should attain at least 60% to gain certification.
What you will learn
Through the Data Science Certification Course Training by Excel R:
- Candidates will get to learn the basic statistics using R & python.
- Association rules & dimension reduction techniques will also be taught.
- Candidates will also be able to understand various clustering algorithms.
- They will be able to learn deployment using R Shiny & streamlit in R & Python as that will also be taught.
- The candidates will also learn Regularization & ensembled techniques as these will also be covered.
- Candidates will thoroughly be able to handle categorical data.
- Basics of MySQL will also be taught to candidates.
- Neural networks & Deep learning will also be thoroughly covered.
- Candidates will understand how to train, test & validate a set.
- Time series analysis will also be covered during analytics for candidates.
Who it is for
The Data Science Certification Course Training by Excel R is highly recommended for:
- Individuals interested in Data Science.
- Data scientists desire for improvement in their skills.
Admission Details
The admission procedure for the Data Science Certification Course Training by Excel R is pretty direct. Candidates are only required to keep their billing details & payment method with them.
Candidates should follow the mentioned steps for admission:
Step 1. Candidates need to visit the homepage of the course: https://www.excelr.com/data-science-certification-course-training
Step 2. Candidates need to select the appropriate mode of learning from Live virtual & self-paced & then click on ‘Buy Now’.
Step 3. In the payment page, click on ‘Proceed’.
Step 4. After entering the correct billing details, candidates are required to do the payment.
Step 5. After the payment is successful, the course will be accessible.
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
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 Studioc
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
Steinbeis University, Berlin Frequently Asked Questions (FAQ's)
There are as such no prerequisites mandatory for the course.
This course is the perfect blend of a live classroom & a self-paced learning programme.
The candidates are provided lifetime access to the course.
The placement assistance programme of Excel R has a gleaming track-record globally.
Candidates are provided access to a vast interview preparation repository.
At least 60% is mandatory upon completion of the course for certification.
For any query, candidates can call toll-free no. 1800-212-2120
Every live lecture is recorded for revision purposes & is accessible by everyone.
The candidates get certified from Tata Consultancy Services & SGIT Alumnus status.
The Data Science Certification Course syllabus is designed for a duration of 6 months.