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Data Analytics course Key Benefits

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October

9:00 AM-1:00 PM Classroom ₹ 57800

Valid till 19 Oct 2019

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Course Description

What is Business Analytics / Data Analytics / Data Science?

Business Analytics or Data Analytics or Data Science certification course is an extremely popular, in-demand profession which requires a professional to possess sound knowledge of analysing data in all dimensions and uncover the unseen truth coupled with logic and domain knowledge to impact the top-line (increase business) and bottom-line (increase revenue). ExcelR’s Data Science curriculum is meticulously designed and delivered matching the industry needs and considered to be the best in the industry. Also, Google Trends shows an upward trajectory with an exponential increase in the volume of searches like never seen before. This is proof enough to back the statements made by Harvard Business Review and the business research giants, that Business Analytics will be the most sought-after profession the world has ever witnessed.

What is a Data Scientist? Or Rather Who is a Data Scientist?

Data for a Data Scientist is what Oxygen is to Human Beings. This is also a profession where statistical adroit works on data – incepting from Data Collection to Data Cleansing to Data Mining to Statistical Analysis and right through Forecasting, Predictive Modeling and finally Data Optimization. A Data Scientist does not provide a solution; they provide the most optimized solution out of the many available. Various research bodies including IBM say that the demand for Data Scientists and AI professionals will skyrocket to 700,000 by 2020. This is very evident with the rise in job opportunities in various job portals. As a Data Scientist or an aspirant, you should not believe us. Go! research on your own and confirm the facts and figures.

Who Should Do The Course?

Professionals who can consider Business Analytics / Data Analytics Certification / Data Science Certificate Program Training as their next logical move to enhance their careers include:

  • Professionals working on Business Intelligence and reporting tools
  • Professionals working on Data Warehouse Technologies
  • Statisticians, Economists, Mathematicians
  • Software programmers (they have an edge in writing code to accomplish a prediction / classification / forecasting model)
  • Business analysts (they have an edge in terms of industry / sector / domain experience)
  • Six Sigma Consultants who already have exposure to Statistics
  • Freshers (market demand is thriving organisations to hire the freshers trained on analytics)

How To Become A Data Scientist?

Accrue knowledge on dealing with data by getting trained and / or certified by any of the well-known institutes which have rich experience working closely with the ever-evolving industry. Knowing about Data Analytics tools or Data Mining software alone will not help you analyse data.

What Else Is Required?

You should possess Domain / Sector / Industry knowledge and learn relevant concepts to strike the right nail. A few such examples which are not limited to those mentioned are:

  • One into web development might want to learn Web Analytics
  • One into search engine optimisation might want to learn Social Media Analytics and Website Analytics
  • One into sales and marketing might want to learn Marketing Analytics, Customer Analytics, Twitter Analytics, Facebook Analytics, Social Media Analytics and Data Collection Tools
  • One into human resources might want to learn Workforce Analytics
  • One into health care might want to learn Healthcare Data Analytics

Real-World Problems

Life Sciences And Health Care – Wearable Devices

  • Many people across the globe are wondering on how to predict diseases in their initial stages and how changing lifestyle habits will cure the disease instead of medication. For this, people started wearing health bands, a few call them sports band which is used to track heartbeat rate, calories burnt, sleeping patterns, number of steps taken (walked) and many more. Jawbone is the most famous wristband and its users have helped generate about 130 million nights of sleep and experts call it the biggest sleep study on the planet. Also recorded are about 1.6 trillion steps and 180 million items of food.

You can tag the data to your personal doctor who will then monitor and inform you of what diseases you are likely to be infected with and what precautions should be taken to avoid it. Sounds WOW!

Retail – Location-Based Analytics

  • Thousands of footfalls are witnessed in any known shopping mall across the globe. Can the store owners within the mall convert the footfalls into revenue? The Answer is ‘YES’! The moment a person connects to free wi-fi accessible in the mall, a unique MAC address is assigned to the person. From that point on, details such as time spent in a store, speed of movement (moving across the wifi zones / range), past buying behaviour, number of times an individual visited a store versus number of times purchase happened and different parameters are checked to send a personalised coupon which will lure the potential customer to become a source of revenue.

The number of coupons sent versus the number of purchases and the number of coupons sent versus the amount purchased is the key Business metrics captured and evaluated to enhance the prediction model. Amazing, isn’t it!

Why You Should Pursue A Career In Data Science / Business Analytics / Data Analytics?

You may question if Data Science Certification is worth it? The answer is yes. Data Science / Analytics is creating myriad jobs in all the domains across the globe. Business organizations realised the value of analysing historical data in order to make informed decisions and improve their businesses. Digitalization in all the walks of business is helping them to generate and analyse the data. This is helping to create many Data Science / Analytics job opportunities in this space. The void between the demand and supply for Data Scientists is huge and hence the salaries pertaining to Data Science are sky high and considered to be the best in the industry. A Data Scientist's career path is long and lucrative as the generation of online data is perpetual and growing in the future. ExcelR offers the best Data Science online certification training along with classroom and e-learning certification courses. The complete Data Science course details can be found in our course agenda on this page.

Data Scientist Training and Placement

Subsequent to the completion of our Data Science Certification program, assignments, projects and placement assistance will kick start. Help will be rendered in terms of resume building, FAQs for the interviews, one-to-one discussion on job description during interview calls, etc. A couple of mock interviews (one on one / telephonic) will be conducted by the SME's to evaluate the grey areas and areas of strength. This helps the participant to retrospect and understand their interview readiness. Participants can attend and successfully crack the interviews with complete confidence. ExcelR offers the best Data Science training and the reviews from our past participant's vouch for our statement.

Things You Will Learn

  • Recap of Demo
  • Introduction to Types of Analytics
  • Project life cycle

Description: Learn about High-level overview of Data Science project management methodology, Statistical Analysis using examples, understand Statistics and Statistics 101. Also, learn about exploratory data analysis, data cleansing, data preparation, feature engineering.

Topics

  • High-Level overview of Data Science / Machine Learning project management methodology
  • Videos for Data Collection - Surveys  and Design of Experiments will be provided
  • The various Data Types namely continuous, discrete, categorical, count, qualitative, quantitative and its identification and application. Further classification of data in terms of Nominal, Ordinal, Interval and Ratio types
  • Random Variable and its definition
  • Probability and Probability Distribution – Continuous probability distribution / Probability density function and Discrete probability distribution / Probability mass function

Description: Continue with the discussion on understanding Statistics, the various Moments of business decision and other Basic Statistics Concepts. Also, learn about some graphical techniques in Analytics.

Topics

  • Balanced vs Imbalanced datasets
  • Various sampling techniques for handling balanced vs imbalanced datasets
  • Videos for handling imbalanced data will be provided
  • What is Sampling Funnel, its application and its components
    • Population
    • Sampling frame
    • Simple random sampling
    • Sample
  • Measure of central tendency
    • Mean / Average
    • Median
    • Mode
  • Measure of Dispersion
    • Variance
    • Standard Deviation
    • Range
  • Expected value of probability distribution

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

  • Measure of Skewness
  • Measure of Kurtosis
  • Various graphical techniques to understand data
    • Bar plot
    • Histogram
    • Box plot
    • Scatter plot
  • Introduction to R and RStudio  
  • Installation of Python IDE
  • Anaconda and Spyder
  • Working with Python and R with some basic commands

Description: Learn about Normal Distribution and Standard Normal Distribution. Rules and Principles of Normal distribution. And how to check for normality by QQ normal distribution Plot.

Topics

  • Normal Distribution
  • Standard Normal Distribution / Z distribution
  • Z scores and Z table
  • QQ Plot / Quantile-Quantile plot

Description: Under this last topic on Basics of statistics, learn some higher statistical concepts and gain understanding on interval estimates.

Topics

  • Sampling Variation
  • Central Limit Theorem
  • Sample size calculator
  • T-distribution / Student's-t distribution
  • Confidence interval
    • Population parameter - Standard deviation known
    • Population parameter - Standard deviation unknown

Introduction to R and Python basic stats

Description: Get introduced to Hypothesis testing, various Hypothesis testing Statistics, understand what is Null Hypothesis, Alternative hypothesis and types of hypothesis testing.

Topics

  • Parametric vs Non-parametric tests
  • Formulating a Hypothesis
  • Choosing Null and Alternative hypothesis
  • Type I and Type II errors
  • Comparative study of sample proportions using Hypothesis testing
  • 2 sample t test

Description: Learn about the various types of tests in Hypothesis testing. Get introduced to the prerequisites and conditions needed to proceed with a Hypothesis test. Understand the interpretation of the results of a Hypothesis testing and probabilities of Alpha error.

Topics

  • 1 sample t test
  • 1 sample z test
  • ANOVA
  • 2 Proportion test
  • Chi-Square test
  • Non-Parametric test

Description: Continuing the discussion on Hypothesis testing, learn more about non-parametric tests. Perform different tests and interpret the results.

Topics

  • Non-Parametric test continued
  • Hypothesis testing using Python and R
  • Scatter Diagram
  • Correlation Analysis
  • Principles of Regression
  • Introduction to Simple Linear Regression
  • R shiny and Python Flask
    • Introduction to R shiny and Python Flask (deployment)
  • Multiple Linear Regression

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

  • Scatter diagram
    • Correlation Analysis
    • Correlation coefficient
  • Ordinary least squares
  • Principles of regression
  • Splitting the data into training, validation and testing datasets
  • Understanding Overfitting (Variance) vs Underfitting (Bias)
  • Generalization error and Regularization techniques
  • Introduction to Simple Linear Regression
  • Heteroscedasticity / Equal Variance

Description: In the second part of the tutorial, you will learn about the Models and Assumptions for building Linear Regression Models, build Multiple Linear Regression Models and evaluate the results of the Linear Regression Analysis.     

Topics

  • LINE assumption
    • Collinearity (Variance Inflation Factor)
    • Linearity
    • Normality
  • Multiple Linear Regression
  • Model Quality metrics
  • Deletion diagnostics

Description: Learn to analyse Attribute Data, understand the principles of Logistic Regression, Logit Model. Learn about Regression Statistics and Logistic Regression Analysis.

Topics

  • Principles of Logistic Regression
  • Types of Logistic Regression
  • Assumption and Steps in Logistic Regression
  • Analysis of Simple Logistic Regression result

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
  • Receiver operating characteristics curve (ROC curve)
  • Lift charts and Gain charts 
  • Lasso and Ridge Regressions

Description: Get introduced to Multinomial regression, or otherwise known as multinomial logistic regression, learn about multinomial logit models and multinomial logistic regression examples.

Topics

  • Logit and Log Likelihood
  • Category Baselining
  • Modeling Nominal categorical data
  • Additional videos are provided on Lasso / Ridge regression for identifying the most significant variables

Data Mining Unsupervised

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

Hierarchial

  • Supervised vs Unsupervised learning
  • Data Mining Process
  • 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
  • Hierarchical Clustering / Agglomerative Clustering

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-clustering
    • K-Means Clustering
    • 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
  • Additional videos are provided to understand K-Medians, K-Medoids, K-Modes, Clustering Large Applications (CLARA), Partitioning Around Medoids (PAM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS)

Description: Learn to apply data reduction in data mining using dimensionality reduction techniques. Gain knowledge about the advantages of dimensionality reduction using PCA and SVD.

Topics

  • Why dimension reduction
  • Advantages of PCA
  • Calculation of PCA weights
  • 2D Visualization using Principal components
  • Basics of Matrix algebra
  • SVD – Decomposition of matrix data

Description: Under data mining unsupervised techniques, learn about Network Analytics and the measures used. Get introduced to Network Analysis tools like NodeXL.

Topics

  • Definition of a network (the LinkedIn analogy)
  • Measure of Node strength in a Network
    • Degree centrality
    • Closeness centrality
    • Eigenvector centrality
    • Adjacency matrix
    • Betweenness centrality
    • Cluster coefficient
  • Introduction to Google Page Ranking

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
  • Sequential Pattern Mining

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
  • SVD in recommendation
  • Vulnerability of recommender systems

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
  • Semantic network
  • Clustering

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

  • LDA
  • Topic Modeling 
  • Sentiment Extraction
  • Lexicons and Emotion Mining

Classifiers

Description: Learn about Machine Learning modeling using KNN, the K nearest neighbour algorithm using KNN algorithm examples. The KNN classifier is one of the most popular classifier algorithms.

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

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: Decision Tree and Random Forest are one of the most powerful classifier algorithms today. Under this tutorial, learn about Decision Tree Analysis, Decision Tree examples and Random Forest algorithms.

Topics

  • Elements of Classification Tree - Root node, Child Node, Leaf Node, etc.
  • Greedy algorithm
  • Measure of Entropy
  • Attribute selection using Information Gain
  • Ensemble techniques
  • Decision Tree C5.0 and understanding various arguments
  • Random Forest and understanding various arguments

Description: Bagging and Boosting is an ensemble technique which is a part of the random forest algorithm. Learn about Bagging and Boosting examples under this tutorial.

Topics

  • Boosting / Bootstrap Aggregating
  • AdaBoost / Adaptive Boosting
  • Stacking
  • Gradient Boosting
  • Extreme Gradient Boosting (XGB)

Description: Artificial Neural Network and Support Vector Machines are the two powerful Deep learning algorithms. Get introduced to Neural Net, Convolutional Neural Network, Recurrent Neural Network. Learn how to work with Support Vector Machine, SVM classifiers and SVM regression.

Topics

  • Artificial Neural Network
  • Biological Neuron vs Artificial Neuron
  • ANN structure
  • Activation function
  • Network Topology
  • Support Vector Machines
  • Classification Hyperplanes
  • Best fit “boundary”
  • Kernel Trick
  • Concept with a business case

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
    • Exponential Smoothing
    • Holts / Double Exponential Smoothing
    • Winters / HoltWinters
  • De-seasoning and de-trending
  • Econometric Models
  • Forecasting Best Practices
  • Forecasting using Python
  • Forecasting using R

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

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

Description:
You will get an introduction to the Python programming language and understand the importance of it. How to download and work with Python along with all the basics of Anaconda will be taught. You will also get a clear idea of downloading the various Python libraries and how to use them.

Topics

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

Basic Python

Description:
In this module, you will learn the basics such as assigning a variable, differences between dictionary, sets, tuple, and some decision making statements. Also, you will learn about working with different loops, data types and its usage.

Topics

  • Data Types
  • Conditional Statements
  • Functions
  • Loops
 

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

Description:
 This module helps you to learn and understand the different libraries used in Python. You will also get a clear idea about the NumPy library and how you can use it. NumPy is a Numeric Python library which helps in dealing with the numeric calculations with data frames.

Topics

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

Working experience with Pandas In Python

Description:
In this module, you will learn how to download the Pandas package and syntax for the same. Pandas is also a library similar to Numpy which predominantly helps in working with series data and data frames. You will learn how to impute the data in the place of missing values called the missing value treatment done in the Pandas package itself.

Topics

  • 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

Description:
 In this module, you will learn where, how and when to use the Matplotlib library. This library is used to visualize the data. You will get an in-depth understanding of the importance of this library.

Topics

  • Introduction to Matplotlib
  • Matplotlib design and different visualizations
 

Description: This module will help you to understand the importance of Seaborn package and downloading the library Just like the Matplotlib library, the Seaborn library is also used in visualizing the data allowing high-level visualizations with categorical data.

Topics

  • 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

Description:
This module will help you to understand the importance of Seaborn package and downloading the library Just like the Matplotlib library, the Seaborn library is also used in visualizing the data allowing high-level visualizations with categorical data.

Topics

  • 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

Description:
 In this module, you will understand the importance of both Scipy and Sklearn libraries which are predominantly used in building Machine Learning Algorithms working with Linear Equations. Sklearn also known as Scikit-learn, is a machine learning library for the Python programming language. You will get a clear idea of where you can use these libraries along with some examples.

Topics

  • Installing both SciPy and Sklearn Libraries
  • Introduction to SciPy (Mathematical Algorithms)
  • Introduction to Sklearn (Machine Learning Algorithms)
  • Introduction to Cloud Computing
  • Difference between On Premise and Cloud
  • Types of Service Models
  • Advantages of Cloud Computing
  • Azure Global Infrastructure
  • Creation of Free tire account inside Azure
  • Sample Instance Creating Both UNIX and Windows and connecting them on cloud
  • Storage options and Creating Extra Storage and attaching to the VMs
  • Blob Storage
  • Creating DB instance
  • Creating Custom VN
  • Brief introduction to Machine Learning Services on Cloud and more
  • 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

Contact Our Team of Experts

Why ExcelR?

Participants Placed Through ExcelR

Testimonials

FAQs

  • The all new and exclusive JUMBO PASS is the latest initiative taken by ExcelR to offer you access to attend unlimited batches over the duration of 365 days. You will be able to attend unlimited number of classes for the course of your choice.
  • Data Analytics, Business Analytics & Data Science are the same with just 3 different names. The name of the role of people working in this profession is called as Data Scientist
  • Data analytics is a profession which caught the attention of the world only since 2 years. Because of this very reason most companies are struggling to close the demand-supply gap. Hence, people who are trained and have decent exposure to the data analytics techniques are recruited immediately.
  • There are a lot of job opportunities in various job portals for freshers. The key thing employer would be keen to know is whether you have the conceptual knowledge or not. The projects provided by ExcelR in various concepts will only reinforce your learning to make you market ready for the jobs.
  • We understand that the human brain can grasp only so much. Hence, we record all the sessions and provide access to the e-learning to the trainees for the rest of their life.
  • Yes and No. Yes in the sense programming skills would be required & No in the sense one need not have extremely strong programming skills. However, we at ExcelR ensure that you get sufficient exposure to the statistical programming tool called ‘R’. We start right from the basics assuming you do not have any exposure towards programming.
  • R has approximately 50% market share & it is open source (free of cost). Hence, R is very lucrative in the analytics space. Almost all the jobs are asking for experience & exposure in R. Demand for other statistical tools is decreasing steadily & hence it is recommended to be futuristic and invest time in learning R.
  • Salaries range varies based on experience, industry, domain, geography & various other parameters. However, as a general thumb rule, we can apply the following formula:
  •  Salary = No. of years of experience * 3 Lakhs per annum (India – INR)
  •  Salary = No. of years of experience * $1200 to $1500 per annum (Overseas – USD)
  • Instructor-led online training is an interactive mode of training where participants and trainer will log in at the same time and live sessions will be done virtually. These sessions will provide scope for active interaction between you and the trainer.
  • ExcelR offers a blended model of learning. In this model participants, can attend classroom, instructor-led live online and e-learning (recorded sessions) with a single enrolment. A combination of these 3 will produce a synergistic impact on the learning. One can attend multiple Instructor-led live online sessions for one year from different trainers at no additional cost with the all new and exclusive JUMBO PASS.
  • The training is a live instructor-led interactive session done at a specific time where participants and trainer will log in at the same time. The same session will be also recorded and access will be provided to revise, recap or watch a missed session.
  • You can reach out to us by visiting our website and interact with our live chat support team. Our customer service representatives will assist you with all your queries. You can also send us an email at [email protected] with your query and our Subject Matter Experts / Sales Team will clarify your queries or call us on 1800-212-2120 (Toll-Free number – India), +1(281) 971-3065 (USA), 800 800 9706 (India), 203-514-6638 (United Kingdom), 128-520-3240 (Australia).
  • Not a problem even if you miss a live Business Analytics session for some reason. Every session will be recorded and access will be given to all the videos on ExcelR’s state-of-the-art Learning Management System (LMS). You can watch the recorded Business Analytics sessions at your own pace and convenience.
  • Yes, after successfully completing the course you will be awarded a course completion certificate from ExcelR.
  • The different payment methods accepted by us are
    • Cash
    • Net Banking
    • Cheque
    • Debit Card
    • Credit Card
    • PayPal
    • Visa
    • Mastercard
    • American Express
    • Discover

Global Presence

ExcelR is a training and consulting firm with its global headquarters in Houston, Texas, USA. Alongside to catering to the tailored needs of students, professionals, corporates and educational institutions across multiple locations, ExcelR opened its offices in multiple strategic locations such as Australia, Malaysia for the ASEAN market, Canada, UK, Romania taking into account the Eastern Europe and South Africa. In addition to these offices, ExcelR believes in building and nurturing future entrepreneurs through its Franchise verticals and hence has awarded in excess of 30 franchises across the globe. This ensures that our quality education and related services reach out to all corners of the world. Furthermore, this resonates with our global strategy of catering to the needs of bridging the gap between the industry and academia globally.

ExcelR's Global Presence

Accolades

 

Data Science Certification Training locations in Hyderabad : Medipalli[500098], A.G.College [500030], HMT Township [500054], Hal Hyderabad [500042], Kondapur [500084], Fatehdarwaza [500065], Begumpet [500016], Lingampalli [500019], Kattedan Ie [500077], Falaknuma [500053], Peerzadiguda [500039], Jillellaguda [500079], I.M.Colony [500082], Gajularamaram [500055], Banjara Hills [500034], Gachibowli [500032], Badangpet [500058], Film Nagar [500096], A.Gs. Staff Quarters [500045], Malakpet Colony [500036], Balanagar Township [500037], Amberpet [500013], Bharat Nagar Colony [500018], Chitkul [502307], Don Bosco Nagar [500086], Gsisr Bandlaguda [500068], Yakutpura [500023], Chaithanyapuri Colony [500060], Chandanagar [500050], Appa Himayathsagar [500008], Hyderabad Jubilee [500002], Meerpet [500097], Humayunnagar [500028], Aie Bollaram [502325], Chetlapotharam [502319], Srinagar Colony [500073], Gandhi Bhawan Hyderabad [500001], Darushifa [500024], Gandhinagar Hyderabad [500080], Sarada Nagar [500070], Barkatpura [500027], Gagan Mahal [500029], Elmela [502300], Sanjeev Reddy Nagar [500038], Manikonda [500089], Cyberabad [500081], Boduppal [500092], Dr.B R Ambedkar O.U [500033], Kphb Colony [500072], Huda Residential Complex [500035], Miyapur [500049], L B Nagar [500074], Ashoknagar Hyderabad [500020], Saidabad Colony [500059], Jntu Kukat Pally [500085], Bowenpally [500011], Icrisat [502324], Administrative Buildings [500007], Aziz Nagar [500075], Afzalgunj [500012], Karwan Sahu [500006],Gaganpahad [500052], Ibrahim Bagh Lines [500031], Putlibowli [500095], Bahadurpura [500064], A.Gs Office [500004], Bachupally [500090], A.I.E. R.C.Puram [502032], Ambernagar [500044].

 

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