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Data Analytics course Key Benefits

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Course Description

ExcelR, as a leading institute of Data science courses in Mumbai , pay a lot of attention to the needs and requirements of the job market by keeping an eye out on the recent trending technologies. One such current trend that has swept the market away is Data Science for which there are myriad data science courses and here one can find the best Data science training. Today, almost every industry, domain and organization in them are using Data science as one of the major tools to help their business grow and adapt according to the demand and preferences of the customers. ExcelR’s curriculum, faculty and post training support is considered to be the best in the industry tailored to the needs of the Data Science job market. ExcelR’s Data Science course agenda has been meticulously designed with R Programming, Python , Machine Learning, Forecasting and Tableau addressing the complete Data life cycle.

What is Data Science & Data Science Course benefits?

In general terms, Data science is a pool of tools and techniques used to simplify the data so that it can be used to make business decisions. Owing to digitalization humongous data generation is in place which is waiting to be understood and visualized in a way that it can be useful by giving some insight into the subject.
Big companies tend to collect the data regarding the customer's behaviors, buying behavior, their likes and dislikes, reviews, etc. which will help them to evaluate their marketing plans, their products and services, all of which one can learn from our data science training in Mumbai. 
In data science, data is collected, cleaned and then visualized to find the hidden patterns and get a prescriptive or a predictive viewpoint. Data science is a mixture of statistics, mathematics, computers, algorithms and some business acumen which makes it approachable and widespread in many ways. We, as the best Data Science Institute in Mumbai, make it a point to train our students, regardless of their domain using the best methods possible, master the concepts and help to build a successful career in the space of Data Science. 
Some of the reasons why data science has become so significant in the organizations and why one should learn it from the best data science training institute are:

  • find new challenges of the market and try to get answers for them
  • helps in understanding the customers in a better way
  • development of products and services
  • find new trends and take actions accordingly
  • test the decisions and make them more refined
  • make quantifiable decisions

Data Science Course in demand 

Data science throughout the last decade has been showing phenomenal growth and that is why one can also experience immense career growth in it as well. This is one of the reasons why our data science face-to-face training and online training has become so popular among new professionals and students. Some of the points proving that data science is highly demanded today are: 

  • According to Glassdoor, the average salary of a data scientist in a company can range from 5-100 lac per annum which is higher than many IT based fields or any field in general. Most of the companies are paying higher salaries to their data scientists almost as comparable to the global standards.
  • As presented by NDTV, data science is a sector that Is facing manpower shortage and therefore making this the best time to grab the opportunity to become a data scientist and get placed in a respected position. Also, data science is supposed to see a growth of almost 8 times by 2025, making it one of the most pursued career choices.
  • According to India today, the average Indian data science position pays around 11 lacs per year. The highest number of jobs is in Bangalore, followed by Delhi, Mumbai, Pune, and others. The companies that hire the data scientists the most are Accenture, IBM, KPMG, Deloitte, Honeywell, Wells Fargo, Amazon, Dell, etc. Mostly finance and banking sectors hire data scientists followed by healthcare, energy, e-commerce, media, etc. 
  • According to Economic times, India has been churning jobs in the field of data science in the last few years. With almost a 400% rise in the job vacancies and requirements and around 1.5 million job openings, the demand is quite high for qualified data science specialists. 
  • According to the Times of India, in the last few years, it is seen that the salary of data scientists has increased by approximately 20%, especially in startups and new ventures. The demand for data scientists has risen to complete various tasks in the tech companies which requires detailing and analyzing large amounts of data for various projects and for developing new products.
  • PWC states that the number of positions for data science is quite high, but the requirement can be incomplete because of the lack of proper skills and knowledge. This is the reason why getting trained and skilled in data science and build a successful career and this why ExcelR is considered to be the  best Data Science Institute in Mumbai
  • Harvard has expressed that data science is a subject that everybody wants to talk about and being a data scientist is also the most lucrative job of the decade. The interested party can capitalize the raw data into something that can catalyze growth for many large-scale businesses.

What are the USPs of ExcelR Data Science Course? 

There are many points as to why students and professionals from all kinds of streams and sectors choose to join us at our data science institute. With our data science program, one can learn all of the details of data science and how to approach the subjects to achieve a promising job in the end. USPs that make us the best Data Science Institute in Mumbai are:

  • Attend as many lectures and batches you want to with the JUMBO PASS throughout the time span of an entire year.
  • Learn from the experienced industry experts with long-term teaching experience and data scientists with hands-on knowledge of the field and market passed down from IIT, IIM, and ISBs. 
  • Take advantage of the classroom programs, live sessions from the instructors and experts and recorded E-Learning videos that can be watched anytime according to one’s own comfort.
  • 2 capstone projects where participant’s will work on full length Data Science lifecycle
  • Practice and hone in on the skills with the help of more than 60 topic wise assignments.
  • Get trained in data science with more than 160 hours of lectures and sessions.
  • Get certified from Tata Consultancy Services (TCS-Ion)
  • Machine Learning and Artificial Intelligence concepts as part of Data Science Course will be provided
  • Get all the help needed after the completion of the course with mock interviews and resume building.
  •  Get full time placement assistance from our data science institute in several companies. 
  • ExcelR offers best Online training for data science Mumbai.

Who can take the Data Science Certification Course?

Data science has become such a pervasive field that almost everybody can be a part of this trend by learning new technologies and skills from our Data Science Institute in Mumbai. Anybody with a knowledge of mathematics, analysis, and business along with strong logical and analytical skills can be a part of this bandwagon of receiving the data science certification
Those who can be successful by joining the data science course are:

  • Business analysts
  • Market analysts
  • Software programmers
  • Statisticians
  • Mathematicians
  • Economists
  • Six Sigma consultants
  • Domain specialists
  • Freshers with good analytical skills

What is Covered in the Data Science Course Curriculum?

 We understand the requirements of the market and what is demanded by the industry today and that is why we do our best to keep our data science training and data science online training module as available as possible. We keep the curriculum updated at all times by adding new technologies and topics of the market. A few of the topics topics covered in the data science course training as part of the curriculum :

  • Python
  • R studio
  • SQL
  • Data Collection
  • Data Cleansing / Feature Engineering/ Exploratory Data Analysis
  • Statistical Analysis
  • Hypothesis Testing 
  • Regression-Linear Regression
  • Logistic Regression
  • Discrete Probability Distribution
  • Advanced Regression
  • Forecasting
  • Data Visualization with Tableau
  • Text Mining
  • Data Mining Supervised- Naïve Bayes
  • Machine Learning 
  • KNN
  • SVD
  • Decision Tree
  • Random Forest
  • Bagging And Boosting
  • Black-box technique-SVM
  • Neural Network
  • Data Mining Unsupervised-Clustering
  • Association Rule
  • Data decomposition Techniques- PCA
  • Natural Language Processing
     

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

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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.

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Data Science Certification Training locations in Mumbai : A I staff colony [400029], Aareymilk Colony [400065], Agripada [400011], Airport [400099], Ambewadi [400004], Andheri [400053], Andheri East [400069], Andheri Railway station [400058], Antop Hill [400037], Asvini [400005], Azad Nagar [400053], B P t colony [400003], B.N. bhavan [400051], B.P.lane [400003], Bandra West [400050], Bandra(east) [400051], Bangur Nagar [400090], Bazargate [400001], Best Staff colony [400012], Bharat Nagar [400007], Bhawani Shankar [400028], Borivali [400091], Borivali East [400066], Borvali West [400092], C G s colony [400013], Central Building [400020], Century Mill Chakala Midc [400093], Chamarbaug [400012], Charkop [400067], Chaupati [400004], Chinchbunder [400009], Chinchpokli [400011], Churchgate [400020], Colaba [400005], Cotton Exchange [400033], Cumballa Hill [400026], Dadar [400014], Dahisar [400068], Danda [400052], Daulat Nagar [400066], Delisle Road [400013], Dharavi [400017], Dockyard Road [400010], Dr Deshmukh marg [400026], Falkland Road [400008], Girgaon [400004], Gokhale Road [400028], Goregaon [400062], Goregaon East [400063], Government Colony [400051], Gowalia Tank [400026], Grant Road [400007], H.M.p. school [400058], Haffkin Institute [400012], Haines Road [400011], Hajiali [400034], Hanuman Road [400057], High Court bulding [400032], Holiday Camp [400005], Irla [400056], Ins Hamla [400095], International Airport [400099], J.B. nagar [400059], J.J.hospital [400008], Jacob Circle [400011], Jogeshwari East [400060], Jogeshwari West [400102], Juhu [400049], Kalachowki [400033], Kalbadevi [400002], Kamathipura [400008], Kandivali East [400101], Kandivali West [400067], Kapad Bazar [400016], Ketkipada [400068], Khar Colony [400052], Kharodi [400095], Kherwadi [400051], Kidwai Nagar [400031], L B s n e collage [400033], Lal Baug [400012], Liberty Garden [400064], M A marg [400008], M.P.t. [400001], Madh [400061], Madhavbaug [400004], Magthane [400066], Mahim [400016], Malabar Hill [400006], Malad [400064], Malad East [400097], Malad West dely [400064], Mandapeshwar [400103], Mandvi [400003], Mantralaya [400032], Marine Lines [400020], Marol Bazar [400059], Masjid [400003], Matunga Railway workshop [400019], Mazgaon [400010], Mori Road [400016], Motilal Nagar [400104], Mumbai Central [400008], Mumbai[400001], N . s.patkar [400007], Nagardas Road [400069], Nagari Niwara [400065], Naigaon [400014], Nariman Point [400021], New Prabhadevi road [400025], New Yogakshema [400021], Noor Baug [400003], Null Bazar [400003], Opera House [400004], Orlem [400064], Oshiwara [400102], Parel [400012], Parel Rly work shop [400003], Prabhadevi [400025], Princess Dock [400009], Rajbhavan [400035], Rajendra Nagar [400066], Ramwadi [400002], Ranade Road [400028], Rani Sati marg [400097], Reay Road [400033], S R p f camp [400060], S Savarkar marg [400028], S V marg [400007], S. c. court [400002], S. k.nagar [400066], Sahar P & t colony [400099], Santacruz Central [400054], Santacruz P&t colony [400029], Santacruz(east) [400055], Santacruz(west) [400054], Secretariate [400032], Seepz [400096], Sewri [400015], Sharma Estate [400063], Shivaji Park [400028], Shroff Mahajan [400002], Stock Exchange [400001], Tank Road [400033], Tardeo [400007], Thakurdwar [400002], Tulsiwadi [400034], V J b udyan [400027], V K bhavan [400010], V.P. road [400052], V.W.t.c. [400005], Vakola [400055], Vesava [400061], Vidyanagari [400098], Vileeparle (east) [400057], Vileparle(west) [400056], Wadala [400031], Worli [400018], Worli Colony [400030].

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