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