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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

This course on Analytics / Data Analytics Certification Training Course in Delhi is a diploma from SGIT, Steinbeis University, Germany. It focuses on data science and teaches various theories related to it using case studies and capstone projects. The syllabus is designed by world class faculty at Steinbeis.

Data analytics is a process to analyze data quantitatively and qualitatively to deduce logical reasons and gather information at one place using machine learning and artificial learning. It is a new way and has a huge scope as every IT sector is now involving data analytics and are demanding data analysts. Students in this course will learn about extraction, data collection, transformation, cleansing, and exploration, statistical analysis, programming languages like R and Python.

After doing this course a candidate will get the status of an alumnus in Steinbeis. They provide placements in companies like Accenture and Infosys. They have a detailed curriculum which covers topics from basics to advanced and a personal mentor and they offer interview preparation sessions as well.

The Highlights

Status of alumnus in Steinbeis
Digital certificate
Live virtual classroom
Certification by EXCELR
In association with Steinbeis University, Germany
Course duration of 6 months
Dual certificate from ExcelR and Steinbeis Akademie

Programme Offerings

Live Session
videos
assignments
Projects
Recorded live sessions

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Steinbeis University, Berlin

Analytics Data Analytics Certification Training Course in Delhi Fees Structure

Course	Fees
Live Virtual Classroom	₹ 51999
Discount	₹ 44999

Eligibility Criteria

Certification qualifying details

A candidate shall be awarded with the certificate post completion of the course. To obtain this certificate candidate is required to take an online examination offered by them and should score 60% or more. After this the candidates can also check their alumnus status.

What you will learn

Data science knowledgeKnowledge of Data mining

The candidate will be able to learn the following things after finishing this course:

A candidate will be able to gain deep knowledge on concepts like regression modelling, hypothesis testing, predictive analytics and many more.
Students will be able to learn machine learning and apply in real life.
Candidate will be introduced to types of analytics.
Students will be able to learn association rules and develop a decision tree.
Candidates will be able to seek information about value-added courses like artificial intelligence, basics of MySQL, R, python and many other similar concepts.
Candidates will develop a hold on the recommender system.
They will learn end-to-end project description with deployment.
Participants will learn regularization techniques.

Who it is for

EconomistStatistician

Following people can enrol for this course:

Business analysts are welcome to take this course.
Professionals who are willing to pursue their career in this field.
Digital marketing professionals are invited to this course.
Software programmers can take up this course.
Freshers who are serious to develop their career in this field.
Six Sigma consultants who are interested in this field.
People who are already employed in business intelligence tools, reporting tools and sectors of data warehousing.
Freshers from any stream who have good analytical and logical skills.

Admission Details

The course takers can follow the procedure given below to get enrolled successfully:

Step 1: Open the webpage of the course with the help of this link: https://www.excelr.com/data-analytics-certification-training-course-in-delhi

Step 2: Choose the course you want to enrol in, live virtual classroom or self-paced.

If you choose to live virtual classroom,

Step 3: Click on enrol now.

Step 4: Choose the time slot you are comfortable in.

Step 5: Enter your details.

Step 6: Make the payment.

If you choose self-paced course,

Step 7: Select the buy now option.

Step 8: Enter your details as asked by them.

Step 9: Secure your payment details.

Step 10: Make the payment.

The Syllabus

Recap of Demo
Introduction to Types of Analytics
Project life cycle
An introduction to our E learning platform

Data Types
Measure Of central tendency
Measures of Dispersion
Graphical Techniques
Skewness & Kurtosis
Box Plot
R
R Studio
Descriptive Stats in R
Python (Installation and basic commands) and Libraries
Jupyter notebook
Set up Github
Descriptive Stats in Python
Pandas and Matplotlib / Seaborn

Random Variable

Probability

Probility Distribution

Normal Distribution

SND

Expected Value

Sampling Funnel

Sampling Variation

CLT

Confidence interval

Assignments Session-1 (1 hr)

Introduction to Hypothesis Testing

Hypothesis Testing with examples

2 proportion test
2 sample t test

Anova and Chisquare case studies

Visualization
Data Cleaning
Imputation Techniques
Scatter Plot
Correlation analysis
Transformations
Normalization and Standardization

Principles of Regression
Introduction to Simple Linear Regression
Multiple Linear Regression

Multiple Logistic Regression

Confusion matrix

False Positive, False Negative
True Positive, True Negative
Sensitivity, Recall, Specificity, F1 score

Receiver operating characteristics curve (ROC curve)

R shiny
Streamlit

Supervised vs Unsupervised learning

Data Mining Process

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

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
Introduction to concepts of CNN

Sources of data

Bag of words

Pre-processing, corpus Document-Term Matrix (DTM) and TDM

Word Clouds

Corpus level word clouds

Sentiment Analysis
Positive Word clouds
Negative word clouds
Unigram, Bigram, Trigram

Vector space Modelling

Word embedding

Document Similarity using Cosine similarity

Extract Data from Social Media

Extract Tweets from Twitter
Extract user reviews of the products from Amazon, Snapdeal and TripAdvisor

Text Analytics using Python

Install Libraries from Shell
Extraction and text analytics in Python

Sentiment Extraction
Lexicons and Emotion Mining

Probability – Recap
Bayes Rule
Naive Bayes Classifier
Text Classification using Naive Bayes

Introduction to time series data

Steps of forecasting

Components of time series data

Scatter plot and Time Plot

Lag Plot

ACF - Auto-Correlation Function / Correlogram

Visualization principles

Naive forecast methods

Errors in forecast and its metrics

Model Based approaches

Linear Model
Exponential Model
Quadratic Model
Additive Seasonality
Multiplicative Seasonality

Model-Based approaches

AR (Auto-Regressive) model for errors

Random walk

ARMA (Auto-Regressive Moving Average), Order p and q

ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q

Data-driven approach to forecasting

Smoothing techniques

Moving Average
Simple Exponential Smoothing
Holts / Double Exponential Smoothing
Winters / HoltWinters

De-seasoning and de-trending

Forecasting using Python and R

Concept with a business case

End to End project Description with deployment using R and Python

Basic Statistics

Data types Identification and probability
Expected values, Measures of central tendencies
Skewness and Kurtosis & Boxplot
Practice Mean, Median, 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 retrieved based on input question

Resume Preparation
Interview Support

Introduction to Big Data
Challenges in Big Data and Workarounds
Introduction to Hadoop and its Components
Hadoop components and Hands-on
Understand the MapReduce (Distributed Computation Framework) and its Drawback
Introduction to Spark
Spark Components
Spark MLlib and Hands-on (one ML model in spark)

Introduction to R Programming

Introduction to R
Data Types in R

How To Install R & R Studio

Data Structures in R

Variable in R
R-Overview
- Vector
- Matrix
- Array
- List
- Data-Frame
Operators in R
- arithmetic
- Relational
- Logical
- Assignment
- Miscellaneous
Conditional 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

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 python

Introduction to Python
Installation of Anaconda Python
Difference between Python2 and Python3
Python Environment
Operators
Identifiers
Exception Handling (Error Handling)

Basic Python

Data Types
Conditional Statements
Functions
Loops

Working with libraries like NumPy, Pandas, Matplotlib, Seaborn, SciPy, Sklearn In Python

NumPy Introduction
Arrays
Array Indexing
NumPy Data Types
Treating Missing and NA’s
Reshaping and combining Arrays

Working experience with Pandas In Python

Pandas Introduction
Basic Operations on Series
Dataframe
Working with Text Data
Working with Missing Data
Indexing and Selecting Data
Merge, Join and Concatenate

Working experience with Matplotlib library In Python

Introduction to Matplotlib
Matplotlib design and different visualizations

Importance of Seaborn package

Introduction to Seaborn Library
Visualizing the Distribution of the Datasets
Plotting the Categorical Data
Visualizing Linear Relationships
Visualizing Statistical Relationships

To work with Seaborn Library (High-level interface for drawing attractive and informative statistical graphics) In Python

Introduction to Seaborn Library
Visualizing the Distribution of the Datasets
Plotting the Categorical Data
Visualizing Linear Relationships
Visualizing Statistical Relationships

Introduction to SciPy and Sklearn Libraries In Python

Installing both SciPy and Sklearn Libraries
Introduction to SciPy (Mathematical Algorithms)
Introduction to Sklearn (Machine Learning Algorithms)

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

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

Steinbeis University, Berlin Frequently Asked Questions (FAQ's)

1: Are there any prerequisites for this course?

No, there are no specific conditions required prior to taking up this course.

2: Whom can I refer to in case of doubts?

In case of query, candidate can contact support system on online chat option provided by them or you can also contact them on 800-212-2120 (toll-free number – India), +1(281) 971-3065 (USA), 800 800 9706 (India), 203-514-6638 (United Kingdom), 128-520-3240 (Australia).

3: Do they accept only cards for payment?

They accept cash, cards, net banking, cheque, Paypal, Visa, MasterCard for accepting payments.

4: What salary packages can i expect after doing this course?

Since this course is in high demand, the employers are willing to pay high salary packages to candidates who have completed this course. MNCs are constantly hiring professionals with this course.

5: What all can I access after enrolling in this course?

The candidates get access to live sessions, recorded sessions, study material for lifetime after getting enrolled.

6: What options do I have if I miss a live session?

The live sessions are recorded for future access. Therefore, if anyone misses a live session they can always resort to recorded sessions.

7: Which companies will hire me after I complete this course?

It sector is actively involved in the hiring process of professionals with this course. MNCs like Accenture are also hiring.

8: Do they have any instalment or scholarship options?

No, they do not have an instalment or scholarship option but an EMI option is available at zero rate interest for live class programme.

9: Do they offer practical training?

Yes, they offer assignments, projects and real life projects which helps the candidate to apply their knowledge in the real world.

10: What are the benefits of certification obtained by this course?

After getting certification from this course recruiters along the globe will recognize you who already have international certification and gain alumnus status at Steinbeis University.