Data Analytics Course And Certification

What is Data Analytics

Data Analytics is the process of examining and analyzing data using various statistical and computational methods to extract insights and valuable information. It involves collecting, cleaning, transforming, and modeling data to identify patterns, trends, and relationships that can be used to make informed business decisions.

Data Analytics includes a range of techniques and tools, such as data mining, machine learning, statistical analysis, and visualization. It can be used to solve various business problems, such as improving customer experience, optimizing marketing campaigns, reducing costs, and increasing revenue.

Data Analytics is widely used in industries such as healthcare, finance, retail, and manufacturing to help organizations gain a competitive edge. It requires a combination of technical and analytical skills, as well as a deep understanding of the business domain and the data being analyzed.

Data Analytics Tools or Softwares

There are several Data Analytics tools available that can help you analyze and extract insights from data. Here are some popular Data Analytics tools:

Microsoft Excel: Excel is a popular tool for Data Analytics because of its powerful data analysis and visualization features. It can handle large datasets and perform complex calculations, and it also has built-in features for generating charts and graphs.

Tableau: Tableau is a powerful data visualization tool that enables you to create interactive dashboards and reports. It has a user-friendly interface and supports various data sources, making it easy to connect to your data.

Python: Python is a versatile programming language that is commonly used for Data Analytics. It has several libraries, such as Pandas, NumPy, and Matplotlib, that make it easy to manipulate and visualize data.

R: R is a programming language that is popular among statisticians and Data Scientists. It has several libraries, such as ggplot2, dplyr, and tidyr, that make it easy to manipulate and visualize data.

SAS: SAS is a powerful Data Analytics tool that is commonly used in industries such as finance and healthcare. It has several features for data manipulation, data analysis, and data visualization.

Apache Spark: Apache Spark is a distributed computing framework that enables you to process large datasets quickly. It has several libraries, such as Spark SQL, Spark Streaming, and Spark MLlib, that make it easy to perform Data Analytics tasks.

Why Study Data Analytics 

Data analytics is an essential aspect of modern business and scientific research. The ability to collect, organize, analyze, and interpret data is crucial for making informed decisions that can improve organizational performance and drive innovation.

Here are some reasons why studying data analytics is essential:

Improve decision-making: Data analytics provides insights into consumer behavior, market trends, and other important metrics that can help businesses make informed decisions.

Increase efficiency: Data analytics helps identify inefficiencies in processes, leading to reduced costs and increased productivity.

Enhance customer experience: Data analytics can provide insights into customer preferences and behavior, leading to more personalized and targeted marketing and customer service.

Drive innovation: Data analytics can identify emerging trends and opportunities, leading to the development of new products, services, and business models.

Gain a competitive advantage: By leveraging data analytics, businesses can gain a competitive advantage over their competitors.

Provide evidence-based research: In scientific research, data analytics can help researchers analyze large datasets to uncover patterns, make predictions, and draw conclusions.

In summary, studying data analytics is crucial for businesses and scientific researchers to make informed decisions, improve efficiency, enhance customer experience, drive innovation, gain a competitive advantage, and provide evidence-based research.

Data Analytics Course Outline

Data Analytics With Excel

Part I: Getting Started with Data Analysis 

Chapter 1: Introducing Excel Tables 

Chapter 2: Grabbing Data from External Sources 

Chapter 3: Scrub-a-Dub-Dub: Cleaning Data 

Part II: PivotTables and PivotCharts 

Chapter 4: Working with PivotTables  

Chapter 5: Building PivotTable Formulas 

Chapter 6: Working with PivotCharts 

Chapter 7: Customizing PivotCharts  

Part III: Advanced Tools 

Chapter 8: Using the Database Functions 

Chapter 9: Using the Statistics Functions 

Chapter 10: Descriptive Statistics 

Chapter 11: Inferential Statistics 

Chapter 12: Optimization Modeling with Solver 

Part IV: The Part of Tens  

Chapter 13: Ten Things You Ought to Know about Statistics  

Chapter 14: Almost Ten Tips for Presenting Table Results and Analyzing Data 

Chapter 15: Ten Tips for Visually Analyzing and Presenting Data 

Data Analytics With R

1. Getting started

Chapter 1. Introduction to R

Chapter 2. Creating a dataset

Chapter 3. Getting started with graphs

Chapter 4. Basic data management

Chapter 5. Advanced data management

2. Basic methods

Chapter 6. Basic graphs

Chapter 7. Basic statistics

3. Intermediate methods

Chapter 8. Regression

Chapter 9. Analysis of variance

Chapter 10. Power analysis

Chapter 11. Intermediate graphs

Chapter 12. Resampling statistics and bootstrapping

4. Advanced methods

Chapter 13. Generalized linear models

Chapter 14. Principal components and factor analysis

Chapter 15. Time series

Chapter 16. Cluster analysis

Chapter 17. Classification

Chapter 18. Advanced methods for missing data

5. Expanding your skills

Chapter 19. Advanced graphics with ggplot2

Chapter 20. Advanced programming

Chapter 21. Creating a package

Chapter 22. Creating dynamic reports 

Data Analytics Video Lectures

Data Analytics Exams and Certificate