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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual 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 | IBMExcelR SolutionsSteinbeis Global Institute, Tubingen |
The Data Science Certification Course Training fee by Excel R is as follows:
The live virtual classroom programme is priced at $ 1,500. But, it is currently available for $ 1,300.
Fee category | Amount in USD |
Self-paced learning | |
Base Fee | $ 1,500 |
Discount | $ 200 |
Total | $ 1300 |
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
- 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 note book
- 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
- 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
- Measure of distance
- Visualization of clustering algorithm using Dendrogram
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 geneal intuition for DBSCAN
- Different parameters in DBSCAN
- Metrics used to evaluate the performance of 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
- Measure of distance / similarity between users
- Driver for recommendation
- Computation reduction techniques
- Search based methods / Item to item collaborative filtering
- 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 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 Propogation
- Intoduction 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 Mininga
- 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, Varience, Standard Deviation and Graphical representations in R
- Creating Python Objects
- Practice Mean, Median, Varience, 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 Rules
- 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
- How to determine which flights would be delayed and by how long?
- Industry : Manufacturing
- Industry : Oil and Gas
- Industry : Automotive
- Industry : Daily Analysis of a product
- Industry : E commerce
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 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
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
- Decision Making
- 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
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 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
- Decision Making
- 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
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
What is Data Visualization?
- Why Visualization came into Picture?
- Importance of Visualizing Data
- Poor Visualizations Vs. Perfect Visualizations
- Principles of Visualizations
- 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)
1: What are the prerequisites for the online Data Science Certification Course?
There are as such no prerequisites mandatory for the course.
2: Is this Data Science Certification Course asynchronous?
This course is the perfect blend of a live classroom & a self-paced learning programme.
3: For how long will the Data Science online certification course be accessible?
The candidates are provided lifetime access to the course.
4: Does Excel R provide placement assistance?
The placement assistance programme of Excel R has a gleaming track-record globally.
5: What more materials do I get apart from the lectures?
Candidates are provided access to a vast interview preparation repository.
6: What is the minimum percentage criteria for certification?
At least 60% is mandatory upon completion of the course for certification.
7: How can I contact Excel R for queries?
For any query, candidates can call toll-free no. 1800-212-2120
8: What if I accidentally miss a live lecture?
Every live lecture is recorded for revision purposes & is accessible by everyone.
9: What are the perks of the certification of this course?
The candidates get certified from Tata Consultancy Services & SGIT Alumnus status.
10: What is the duration of the live modules of the course?
The Data Science Certification Course syllabus is designed for a duration of 6 months.
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