Course Description
Data Science Certification from SGIT, Steinbeis University, Germany:
Accelerate your career with Data Science certification from SGIT, Steinbeis University Germany , one of the leading universities in Germany. This course is a perfect blend of theory, case studies and capstone projects. The course curriculum has been designed by Steinbeis University and considered to be the best in the industry. Get noticed by recruiters across the globe with the international certification. Post certification one will gain the alumnus status in Steinbeis University.
What is the certification process?
Post completion of the training, one should take an online examination facilitated by the university and should attain 60% or more to complete the course and gain the certification. Subsequently participants can check their alumnus status on SGIT , Steinbeis Global Institute Tübingen.
Advanced Certification Program in Data Science and AI for Digital Transformation from IITM Pravartak:
ExcelR, in association with IIT Madras, brings to you an add-on certification for your Data Science Course.
This certification program provides you with:
- 15+ Hours of Interactive Live-Virtual Sessions by professors of IIT Madras.
- Optional 2-day Campus Immersion in the beautiful, state-of-the-art IIT Madras.
- A prestigious IIT Madras Pravartak Certificate.
What is the certification process?
During the period of your course, interactive live-virtual sessions will be conducted by professors of IIT Madras. An optional campus immersion will also be planned, whereby a slot will be created, and you will travel to Chennai for a two-day experience at the IIT Madras campus. Post training, you will take a short quiz on the topics discussed in the session, which will unlock your Advanced Certification in Data Science and AI for Digital Transformation from IITM Pravartak.
Data Science Course Training
ExcelR offers Data Science course, the most comprehensive Data Science course in the market, covering the complete Data Science lifecycle concepts from Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization and deploying the solution to the customer. Skills and tools ranging from Statistical Analysis, Text Mining, Regression Modelling, Hypothesis Testing, Predictive Analytics, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, Tableau, Spark, Hadoop, programming languages like R programming, Python are covered extensively as part of this Data Science training. ExcelR is considered as the best Data Science training institute which offers services from training to placement as part of the Data Science training program with over 400+ participants placed in various multinational companies including E&Y, Panasonic, Accenture, VMWare, Infosys, etc. ExcelR imparts the best Data Science training and considered to be the best in the industry.
Why Should You Choose ExcelR For Data Science Training?
If you are serious about a career pertaining to Data science, then you are at the right place. ExcelR is considered to be one of the best Data Science training institutes. We have built careers of thousands of Data Science professionals in various MNCs in India and abroad. “Training to Job Placement” – is our niche. We do the necessary hand-holding until you are placed. Our expert trainers will help you with upskilling the concepts, to complete the assignments and live projects.
ExcelR has a dedicated placement cell and has partnered with 150+ corporates which will facilitate the interviews and help the participants in getting placed. ExcelR is the training delivery partner in the space of Data Science for 5 universities and 40+ premier educational institutions like IIM, BITS Pilani, Woxen School of Business, University of Malaysia, etc. Faculty is our strength. All of our trainers are working as Data Scientists with over 15+ years of professional experience. Majority of our trainers are alumni of IIT, ISB and IIM and a few of them are PhD professionals. Owing to our faculty, ExcelR’s certification is considered to be the best Data Science certification offered in this space. ExcelR offers a blended learning model where participants can avail themselves classroom, instructor-led online sessions and e-learning (recorded sessions) with a single enrollment. A combination of these three modes of learning will produce a synergistic impact on learning. One can attend an unlimited number of instructor-led online sessions from different trainers for 1 year at no additional cost. No wonder ExcelR is regarded as the best Data Science training institute to master Data Science concepts and crack a job.
What Is Data Science? Who Is Data Scientist?
Data Science is all about mining hidden insights of data pertaining to trends, behaviour, interpretation and inferences to enable informed decisions to support the business. The professionals who perform these activities are said to be a Data Scientist / Science professional. Data Science is the most high-in-demand profession and as per Harvard and the most sort after profession in the world.
Why One Should Take The Data Science Course?
Is Data Science certification being worth pursuing as a career?
The answer is a big YES for myriad reasons. Digitalization across the domains is creating tons of data and the demand for the Data Science professionals who can evaluate and extract meaningful insights is increasing and creating millions of jobs in the space of Data Science. There is a huge void between the demand and supply and thereby creating ample job opportunities and salaries. Data Scientists are considered to be the highest in the job market. Data Scientist career path is long-lasting and rewarding as the data generation is increasing by leaps and bounds and the need for the Data Science professionals will increase perpetually.
- 1.4 Lakh jobs are vacant in Data Science, Artificial Intelligence and Big Data roles according to NASSCOM
- The world will notice a deficit of 2.3 Lakh Data Science professionals by 2021
- The Demand for Data Scientist professionals has increased by 417% in the year 2018, in India, as per the Talent Supply Index
- Data Science is the best job to pursue according to Glassdoor 2018 rankings
- Harvard Business Review stated that ‘Data Scientist is the sexiest job of the 21st century’
You May Question If Data Science Certification Is Worth It?
The answer is yes. Data Science / Analytics creating myriad jobs in all the domains across the globe. Business organizations realised the value of analysing the historical data in order to make informed decisions and improve their business. Digitalization in all the walks of the business is helping them to generate the data and enabling the analysis of the data. This is helping to create myriad data science/analytics job opportunities in this space. The void between the demand and supply for the Data Scientists is huge and hence the salaries pertaining to Data Science are sky high and considered to be the best in the industry. Data Scientist career path is long and lucrative as the generation of online data is perpetual and growing in the future.
Why ExcelR Is The Best Data Science Training Institute?
ExcelR offers the best Data Science certification online training along with classroom and self-paced e-learning certification courses. The complete Data Science course details can be found in our course agenda on this page.
Who Should Do The Data Science Course?
Professionals who can consider Data Science course as a next logical move to enhance in their careers include:
- Professional from any domain who has logical, mathematical and analytical skills
- Professionals working on Business intelligence, Data Warehousing and reporting tools
- Statisticians, Economists, Mathematicians
- Software programmers
- Business analysts
- Six Sigma consultants
- Fresher from any stream with good Analytical and logical skills
Interview Preparation Sessions
Participants who have completed the Data Science course training and the projects will be put under our Placement Incubation Program. As part of this program, participants will undergo a thorough interview preparation process on Data Science. A huge repository of Data Science Interview questions with answers will be provided for the participants to prepare. A dedicated Data Science Subject Matter Expert (SME) will help in resume building, conduct mock interviews and evaluate each participant's knowledge, expertise and provide feedback. Our SMEs will do the necessary handholding on interview preparation process till the time the participant is placed. Guidance is also provided on Linkedin profile building and tricks of the trade to improve the marketability of the resume. - ExcelR Management
Projects
- As more and more people are expressing their views and opinions on various microblogging websites about various products and services. There has been a surge of data generated by the users, these websites have people sharing their thoughts daily.
- Sentiment Analysis with the help of Natural Language Processing technique for identifying the sentiments of a product or service
- Customers are looking for more information before buying a product on E-commerce websites. Amazon introduced a new feature 'question and answer' search field for products.
- The project is to build an information retrieval system from Amazon products data based on NLP techniques. Top 5 relevant answers to be retrieved based on input question
- Reducing the risk of fraudulent loans by carefully evaluating the risk & at the same time increasing profits by rejecting only those loans, which have the potential of defaulting
- The objective of the analysis to predict an item when sold, what is the probability that customer would file for warranty and to understand important factors associated with them
- Predict which flights would be delayed and by how long?
- Flight delays cost the industry an estimated $25 billion every year More than 60 percent of frequent flyers cite delays among the things about air travel that they find most dismaying. And the costs are spread around - an extra $25 in parking here, a missed business meeting there. Carriers, meanwhile, pay an estimated $62 per minute in crew, fuel, maintenance and other costs. It adds up.
Career Progression and Salary Trends
Learning Path
Course Curriculum
- Recap of Demo
- Introduction to Types of Analytics
- Project life cycle
- An introduction to our E learning platform
Description:Learn about the other moments of business decision as part of Statistical Analysis. Learn more about Visual data representation and graphical techniques. Learn about Python, R programming with respect to Data Science and Machine Learning. Understand how to work with different Python IDE and Python programming examples.
Topics
- 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
Topics
- 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
Description: Learn about Linear Regression, components of Linear Regression viz regression line, Linear Regression calculator, Linear Regression equation. Get introduced to Linear Regression analysis, Multiple Linear Regression and Linear Regression examples.
Topics
- Principles of Regression
- Introduction to Simple Linear Regression
- Multiple Linear Regression
Description: Learn about the Multiple Logistic Regression and understand the Regression Analysis, Probability measures and its interpretation. Know what is a confusion matrix and its elements. Get introduced to “Cut off value” estimation using ROC curve. Work with gain chart and lift chart.
Topics
- Multiple Logistic Regression
- Confusion matrix
- False Positive, False Negative
- True Positive, True Negative
- Sensitivity, Recall, Specificity, F1 score
- Receiver operating characteristics curve (ROC curve)
Description: Learn deployment using Rshiny and streamlit in R and python
Topics
- R shiny
- Streamlit
Description:As part of Data Mining Unsupervised get introduced to various clustering algorithms, learn about Hierarchial clustering, K means clustering using clustering examples and know what clustering machine learning is all about.
Topics
- 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
Description:In this continuation lecture learn about K means Clustering, Clustering ratio and various clustering metrics. Get introduced to methods of making optimum clusters.
Topics
- 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
Description:Introduction to Density based clustering method
Topics
- A geneal intuition for DBSCAN
- Different parameters in DBSCAN
- Metrics used to evaluate the performance of model
- Pro's and Con's of DBSCAN
Description:Learn to apply data reduction in data mining using dimensionality reduction techniques. Gain knowledge about the advantages of dimensionality reduction using PCA and tSNE
Topics
- PCA and tSNE
- Why dimension reduction
- Advantages of PCA
- Calculation of PCA weights
- 2D Visualization using Principal components
- Basics of Matrix algebra
Description:Learn one of the most important topic Association rules in data mining. Understand how the Apriori algorithm works, and the association rule mining algorithm.
Topics
- What is Market Basket / Affinity Analysis
- Measure of association
- Support
- Confidence
- Lift Ratio
- Apriori Algorithm
Description:Learn how online recommendations are made. Get insights about online Recommender System, Content-Based Recommender Systems, Content-Based Filtering and various recommendation engine algorithms. Get to know about people to people collaborative filtering and Item to item collaborative filtering.
Topics
- 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
Description:Decision Tree and is one of the most powerful classifier algorithms today. Under this tutorial learn the math behind decision tree algorithm with a case study
Topics
- 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
Description: Learn about how to handle categorical data using different methods
Topics
- Encoding Methods
- OHE
- Label Encoders
- Outlier detection-Isolation Fores
- Predictive power Score
Description: It helps in reducing overfitting , training time and it improves accuracy
Topics
- Recurcive Feature Elimination
- PCA
Description:Here you are going to learn what are they ways to improve the models interms of accuracy and reducing overfitting ( Bias vs Variance )
Topics
- Splitting data into train and test
- Methods of cross validation
- Accuracy methods
Description:Rather working on a single model we can work on a diverse set of models it can achieved by using Ensemble learning
Topics
- Bagging
- Boosting
- Random Forest
- XGBM
- LGBM
Description:KNN and SVM: KNN algorithm is by far one of the easiest algorithms to learn and interpret. SVM is another most popular algorithm best part is it can be used for both classification and regression purpose, learn these two by using simple case studies
Topics
- 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
Description: Neural Networks: It is a supervised machine learning algorithm which mimics our human brain and it is foundation for Artificial Intelligence and Deep Learning. Here you learn the operation of neural networks using R and Python.
Topics
- 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
Description: Text mining or Text data mining is one of the wide spectrum of tools for analyzing unstructured data. As a part of this course, learn about Text analytics, the various text mining techniques, its application, text mining algorithms and sentiment analysis.
Topics
- 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
Description: Learn how to extract data from Social Media, download user reviews from E-commerce and Travel websites. Generate various visualizations using the downloaded data.
Topics
- Extract Tweets from Twitter
- Extract user reviews of the products from Amazon, Snapdeal and TripAdvisor
Description: Learn how to perform text analytics using Python and work with various libraries that aid in data extraction, text mining, sentiment analysis and
Topics
- Install Libraries from Shell
- Extraction and text analytics in Python
Description: Natural language processing applications are in great demand now and various natural language processing projects are being taken up. As part of this tutorial, learn about Natural language and ‘Natural language understanding’.
Topics
- Sentiment Extraction
- Lexicons and Emotion Mining
Description: Under the Naive Bayes classifier tutorial, learn how the classification modeling is done using Bayesian classification, understand the same using Naive Bayes example. Learn about Naive Bayes through the example of text mining.
Topics
- Probability – Recap
- Bayes Rule
- Naive Bayes Classifier
- Text Classification using Naive Bayes
Description: Forecasting or Time Series Analysis is an important component in analytics. Here, get to know the various forecasting methods, forecasting techniques and business forecasting techniques. Get introduced to the time series components and the various time series analysis using time series examples.
Topics
- 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
Assignments/Projects/Placement Support
- 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
Predicting the flight delays
- How to determine which flights would be delayed and by how long?
- Industry : Manufacturing
Predict impurity in ore
- The main goal is to use this data to predict how much impurity is in the ore concentrate As this impurity is measured every hour if we can predict how much silica (impurity) is in the ore concentrate, we can help the engineers giving them early information to take actions
- Industry : Oil and Gas
Predicting the oil price
- Oil production and prices data are for 1932-2014(2014 data are incomplete );gas production and prices are for 1955-2014 export and net export data are for 1986-2013
- Industry : Automotive
Electric Motor Temperature
- Predict the temperature of rotor and stator of E-Motor
- Industry : Daily Analysis of a product
"Daily" Twitter Data Analysis for a Product
- Sentiment Emotion mining of twitter data of new product
- Industry : E commerce
Natural Language Processing
- Top 5 relevant answers to be retrived based on input question
- Resume Preparation
- Interview Support
Value added courses
- 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
- 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
Topics
- 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
Topics
- Train, Test & Validation Set
- Vanishing & Exploding Gradient
- Dropout
- Regularization
- Optimization algorithm
- Learning Rate
- Tuning
- Softmax
CNN
Topics
- CNN
- Deep Convolution Model
- Detection Algorithm
- Face Recognition
RNN
Topics
- 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
Contact Our Team of Experts
