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.
In this modern era, Data Analytics provides a subtle way to analyse the data in a qualitative and quantitative manner to draw logical conclusions. Gone are the days where one has to think about gathering the data and saving the data to form the clusters. For the next few years, it’s all about Data Analytics and it’s techniques to boost the modern era technologies such as Machine learning and Artificial Intelligence.
At ExcelR, the Data Analytics course curriculum provides extensive knowledge of Data Collection, Extraction, Cleansing, Exploration, and Transformation. Alongside the Data Mining, Data Integration is done with feature Engineering to build Prediction models for Data Visualization and deploying the solution. You name the skillset and our trainers are always there to handle the new generation tools with latest versions. As a part of the Data Analytics training, the range of skills and tools that are emphasized in the course include 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 and Python.
Being one of the best Data Analytics Institutes, our Data Analysts from IIT and ISB offer Data Analytics certification course with tailor-made course curriculum to suit the professionals as well as freshers. More than 400+ participants are successfully placed in top MNC's namely E&Y, Accenture, VMWare, Infosys, IBM etc.
Why Should You Choose ExcelR For Data Analytics Certification Course in Mumbai?
If you are looking to make a prospective career in Data Analytics, your choice is right with Excel. Not only certification, but ExcelR also provides real-time projects that involve Data Analytics with a combination of R and Python.
Well, with our training, participants can excel in their career as a Data Analyst and statistics will stand as proof of our quality services. We have guided thousands of people as Data Analytics experts in MNCs across India and foreign countries. “Training to Job Placement” that is what we do. Our Expert trainers will guide you in up-skilling the concepts, completing the assignments and live projects. With a dedicated placement cell, ExcelR has partnered with 150+ corporates to facilitate interviews on Data Analytics and help our participants to get placed in the top MNC's.
In the space of Corporate Training, we are delivering the best in class training to top MNC's such as IBM and many more. ExcelR is a Data Analytics training delivery partner for over 5 universities, namely IIM, ISB, BITS Pilani and Woxen School of Business and 40+ premier educational institutions. All our trainers are Data Analytics experts with 15+ years of experience and they hail from IIT, ISB and IIM. Few of our trainers have also successfully completed their Phd in Data Analytics.
Speaking about our training method, ExcelR delivers unique live interactive sessions with recordings. Both our online and classroom participants can avail the recorded sessions with a single enrolment. We maintain the data in our servers and all our participants can access the data through our portal at any point of time. Our participants can attend the online sessions throughout the year at no additional cost.
What is Data Analytics? Who Is Data Analyst?
The math behind the Data Analytics is resolved. Data Analytics is the mining of the data for analysing it through qualitative and quantitative methods to interpret meaningful insights. These insights will be helpful while taking critical decisions to improve the efficiency of the business output. Data Analyst is a professional who performs these activities to reveal the hidden truths from the selected data. As per the Harvard study, Data Analytics will be a highly demanded profession in the next few years in all parts of the world.
Why Should You Take Data Analytics Courses in Mumbai?
Most of us will have a lot of questions surrounded in our mind.
Is Data Analytics Certification Worth Enough To Begin a Career With?
Out of the myriad reasons, the answer would be a big “Yes”. Data Analytics is the NextGen Tech that plays an important role in the Machine Learning and Artificial Intelligence. With automation and robotics capturing the attention of the IT industry, there will be huge scope for the Data Analysts in the market. The professionals with Data Analytics certification will have a huge demand in the IT sector. Digitalization, data capturing, data evaluation, extraction of meaningful insights; all in all are going to create millions of jobs for the Data Analysts in the near future.
It’s never too late!!! This is the right time to make a decision for joining the Data Analytics certification course. There is a huge demand and supply gap in the market for data analysts. The Data Analyst career is going to create a long lasting career for every individual who chose this path. The reason being, the data generation is going to increase by many folds creating ample job opportunities for the Data Analysts.
- As per the NASSCOM, around 1.4 Lakh positions were vacant in the field of big data, Data Analytics and Artificial Intelligence.
- By the year 2021, the void might increase and reach a deficit of around 2.3 lakh positions.
- As per the Talent supply index, there is a steep increase of 417% for data analyst professionals in India.
- As per the Glassdoor Rankings 2018, Data Analytics is the best-rated job.
- Harvard quoted in a business review that “Data Analyst Job is the going to be the sexiest job in the 21st century”.
Why You Should Pursue A Career In Data Analytics?
As mentioned by several recognized review sites, Data Analytics is going to be the revolution in the market. Data Analyst is a profession that is going to become a craze for the following generations. Now that the digitalization has fastened the things and the organizations started recognizing the importance of analysing the historical data to take formal decisions for insightful growth in their business prospect. With a huge requirement of Data Analysts and the considerable skill gap, this has increased the packages in analytics and it is going to perpetuate in the near future and we are able to witness that more organizations are going to join the Data Analytics. ExcelR offers the best Data Analytics certification online training along with classroom and self-paced e-learning certification courses. The complete Data Analytics course details can be found in our course agenda on this page.
Who Should Do The Data Analytics Course?
- Professionals or fresher’s who are really serious about making a career in Data Analytics can do this course
- Individuals from any domain who possess logical thinking about mathematical and analytical skills.
- People who are working on business intelligence tools, data warehousing and reporting tools.
- Statisticians, Economists, Mathematicians
- Software programmers
- Business Analysts
- Six Sigma Consultants
- Digital Marketing professionals
- Freshers 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
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
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