R packages/librariesData Analytics Certification Course with R, Excel and Tableau


Data analytics never seems to get old. Every organization big or small requires its professionals to be well equipped with performing Data analytics with R, Excel and Tableau.

Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns and techniques vary according to organizational requirements.

Role of Data Analytics:

Mapping and tracing data from system to system in order to solve a given business or system problem. Design and create data reports and reporting tools to help business executives in their decision making, Perform statistical analysis of business data.

Difference between data Analytics and Data Analysis:

Data Analysis and data analytics are often treated as interchangeable terms, but they hold slightly different meanings. Data analysis is the overarching Data Analyst practice that encompasses the use of data analytics tools and techniques to achieve business objectives.

Data analysis is a broader term that refers to the process of compiling and analyzing data in order to present findings to management to help inform business decision making. Data analytics is a sub component of data analysis that involves the use of technical tools and data analysis techniques.

Which data analytics tools are used by Data Analysts?

  • Tableau Public
  • OpenRefine
  • RapidMiner
  • Google Fusion Tables
  • NodeXL
  • Wolfram Alpha
  • Google Search Operator

Data analysis is the process of examining, transforming, and arranging raw data in a specific way to generate useful information from it. In essence, data analysis allows for the evaluation of data through analytical and logical reasoning to lead to some sort of outcome or conclusion in some context. It is a multi-faceted process that involves a number of steps, approaches, and diverse techniques. The approach you take to data analysis depends largely on the type of data available for analysis and the purpose of the analysis.

Data analytics never seems to get old. Every organization big or small requires its professionals to be well equipped with performing Data analytics with R, Excel and Tableau.

Students can build portfolios which will help them land a job they prefer. The data analysis courses contain all the information and knowledge packaged

Learning Pathway with I Smart Learning (Course Curriculum) 

Unit 1: Programming in R

This course covers advanced topics in R programming that are necessary for developing powerful, robust and reusable data science tools.

  • Functional programming in R
  • Robust error handling
  • Object oriented programming
  • Profiling and bench marking
  • Proper design of functions.
  • Conditional and Loops
  • R Packages/ Libraries
  • Data mining GUI in R
  • Data Structures in R
  • Exceptions/ Debugging in R

Unit 2: Data Wrangling

  • 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

Unit 3: Statistics and Probability

Simply put, probability is an intuitive concept. We use it on a daily basis without necessarily realizing that we are speaking and applying probability to work.

Life is full of uncertainties. We don’t know the outcomes of a particular situation until it happens. Will it rain today? Will I pass the next math test? Will my favorite team win the toss? Will I get a promotion in next 6 months? All these questions are examples of uncertain situations we live in. Let us map them to few common terminology which we will use going forward.

  • Experiment – are the uncertain situations, which could have multiple outcomes. Whether it rains on a daily basis is an experiment.
  • Outcome is the result of a single trial. So, if it rains today, the outcome of today’s trial from the experiment is “It rained”
  • Event is one or more outcome from an experiment. “It rained” is one of the possible events for this experiment.
  • Probability is a measure of how likely an event is. So, if it is 60% chance that it will rain tomorrow, the probability of Outcome “it rained” for tomorrow is 0.6

Why do we need probability?

In an uncertain world, it can be of immense help to know and understand chances of various events. You can plan things accordingly. If it’s likely to rain, I would carry my umbrella. If I am likely to have diabetes on the basis of my food habits, I would get myself tested. If my customer is unlikely to pay me a renewal premium without a reminder, I would remind him about it.

So knowing the likelihood might be very beneficial.

Topics covered are:

  • 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

Unit 4: Modeling in R

  • Linear and Non Linear regression model in R
  • Multiple linear regressions model
  • Representation of regression results
  • Tree-based regression models
  • Decision tree-based models
  • Rule-based systems

Unit 5: Mining Algorithms Using R

  • Association analysis and 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 and Covariance

Unit 6: Time Series Forecasting in R and Model Deployment

  • Basics and Components of time series
  • Time series forecasting
  • Deploying predictive models
  • Using SQL server, external tools and big data tools
  • Integrating R with Hadoop / Spark

Unit 7: Data Wrangling using SQL and Excel

Here you’ll learn to use Structured Query Language (SQL) to extract and analyze data stored in databases. You’ll first learn to extract data, join tables together, and perform aggregations. Then you’ll learn to do more complex analysis and manipulations using sub queries, temp tables, and window functions. By the end of the course, you’ll be able to write efficient SQL queries to successfully handle a variety of data analysis tasks.

  • SQL queries.
  • Integrating with R.
  • Deployment and execution
  • Data modeling and formatting using Excel.
  • Excel formulas to perform analytics.
  • Macros for job automation.

Unit 8: Data Analysis and Visualization using Tableau

Data Visualization in Tableau:-

Tableau is a Data Visualization tool that is widely used for Business Intelligence but is not limited to it. It helps create interactive graphs and charts in the form of dashboards and worksheets to gain business insights.

Tableau is a data visualization tool created by tableau software. It allows for rapid insight by transforming data into dashboards that look amazing and are also interactive.

 Data Visualization techniques:-

Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science.

  • 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

Unit 9: Projects

Our Trainers:

Michael Crawley: Holds a Degree with JOHNS HOPKINS University

Course Curriculum

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