Data Analytics with R, Excel, Tableau Course - Classroom Training

Online bootcamp to become a Data Analyst using R, Excel and Tableau

Course Description

You will learn Data Analytics and Data Visualization tools. You will learn R, Tableau and Excel primarily, and bits of Hadoop, Spark, SQL and other big data technologies additionally. Acadgild provides quality training to anyone with the zeal to learn. We offer a great platform for you to master Data Analytics. Our courses have world class content, offer a gamified learning experience, and include real-time projects to help you gain practical exposure. Additionally, you will be mentored by leading experts in the industry, and will have support around-the-clock from SMEs (Subject Matter Experts) to help you resolve your queries. The course will make you proficient in: Data Manipulation Exploratory Data Analysis Data Visualization Linear Models Data Analytics with Visualization using Tableau You don’t need... Read More »

You will learn Data Analytics and Data Visualization tools. You will learn R, Tableau and Excel primarily, and bits of Hadoop, Spark, SQL and other big data technologies additionally.

Acadgild provides quality training to anyone with the zeal to learn. We offer a great platform for you to master Data Analytics. Our courses have world class content, offer a gamified learning experience, and include real-time projects to help you gain practical exposure. Additionally, you will be mentored by leading experts in the industry, and will have support around-the-clock from SMEs (Subject Matter Experts) to help you resolve your queries.

The course will make you proficient in:

  • Data Manipulation
  • Exploratory Data Analysis
  • Data Visualization
  • Linear Models
  • Data Analytics with Visualization using Tableau

You don’t need to follow this course with another. This course covers most of the skills required to help you land a lucrative job as a data professional.

 

Job Placement Assistance Program

Our job placement program offers students one-on-one career counselling, and the chance to work with our corporate partners.

Candidates who fulfill the following criteria will be eligible for the program:

  • Scored 75% marks or above (resulting in a Platinum certificate) in the course
  • Successfully completed at least 2 quality projects
  • Scored 80% in all the mock technical interviews
  • Was never found plagiarizing code

 

Course Prep – To bring you up to speed

Phase One – The First Steps
Setting Up: Enroll for Course, Access Dashboard, Meet Mentor, Attend Orientation

Phase Two – The Training
Practice Drills: Live Sessions, Live Coding with Mentors, Case Studies & Assignments, Capstone Project

Phase Three – The Launchpad
Career Preparation: Resume Building, Reputation Management, Mock Interviews, Networking

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Course Outcomes:
  • Intensive 4-month Program
  • Collaborative Assignments with Mentors
  • Master Statistics, Machine Learning and Data Visualization
  • Learn Tools like R, Excel and Tableau
Course Details:

Target Audience

The course is suited for anyone with a zeal to learn about data analytics. It is ideal for aspiring data analysts from different backgrounds. Our students are generally analysts, developers, managers, information architects, researchers, and other working professionals looking to advance in the field of data analytics.

Access Timeframe

Lifelong Access to Course Material

Prerequisites

You need knowledge of math and statistics. Knowledge of R programming language would be a plus. Having said that, the course covers the basics of R.
Certificate Info:

Type of Certification

International Certification

Format of Certification

Digital

Method of Obtaining Certification

You will get an internationally recognized certificate if you successfully complete the course.

Additional Details

Certificates are issued according to performances in assignments and projects.

Course Outline

  • Basics of R
  • Conditional and loops
  • R packages/libraries
  • Data mining GUI in R
  • Data structures in R
  • Exceptions/ debugging in R
  • Reading CSV, JSON, XML, .XLSX and HTML files using R
  • ETL operations in R
  • Sorting/ merging data in R
  • Cleaning data
  • Data management using dplyr in R
  • Descriptive statistics, random variables, and probability distribution functions
  • Data distributions like uniform, binomial, exponential, poisson, etc.
  • Probability concepts, set theory and hypothesis testing
  • Central limit theorem, t-test, chi-square, z-test
  • Central limit theorem
  • ANOVA
  • Linear regression model in R
  • Multiple linear regressions model
  • Representation of regression results
  • Non-linear regression models
  • Tree-based regression models
  • Decision tree-based models
  • Rule-based systems
  • Association analysis
  • Market-based analysis/ rules
  • Apriori algorithm
  • Ensemble models - random forest model, boosting model
  • Segmentation analysis- types of segmentation, k-means clustering, Bayesian clustering
  • Feature selection/ dimension reduction- multidimensional scaling, dimension reduction, factor or component analysis
  • Axes
  • Covariance
  • Basics of time series
  • Components of time series
  • Time series forecasting
  • Deploying predictive models
  • Using SQL server
  • Using external tools
  • Using big data tools
  • Integrating R with Hadoop/Spark
  • SQL queries
  • Integrating with R
  • Deployment and execution
  • Data modeling and formatting using Excel
  • Excel formulas to perform analytics
  • Macros for job automation
  • Introduction to Tableau and its layout
  • Connecting tableau to files and databases
  • Data filters in Tableau
  • Calculation and parameters
  • Tableau graphs and maps
  • Creating Tableau dashboard
  • Data blending
  • Creating superimposed graphs
  • Integrating Tableau with R

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