Data Analytics Essentials

The first module of this data analytics course for beginners will cover the basics of Microsoft Excel. Students will learn data analysis essentials using Excel to create and format spreadsheets, along with CSV, tables, formulae, sorting, filtering, and much more.

• Why Excel? What are the advantages of Excel?
  Here, students will learn why Excel is a powerful spreadsheet application for analyzing and manipulating data and the advantages of using Excel for business and personal use.
• CSV File Format
  CSV files can be used with almost any spreadsheet program, such as Microsoft Excel, Apache Openoffice Calc, or Google Sheets. Here, students will learn how to use CSV for exchanging data between different applications.
• Tools, Ribbons, Commands
  In this, students will learn how to add functionality to a workbook and make working with data easier using tools, ribbons, and commands available in Excel.
• Cell Referencing
  This topic will teach students the process of cell referencing, a powerful feature in Excel that allows them to link data from multiple sheets and workbooks.
• Tables
  This topic will teach students how to implement tables in Excel to organize data and make it easy to view and understand.
• Basic Arithmetic Functions (+,-,*,/)
  This topic will make students familiar with implementing essential arithmetic functions to create more complex formulas that will unlock the power of Excel for data analysis needs.
• Date Functions
  This topic will familiarize students with implementing date functions using different formats in Excel.
• Sorting
  Here, students will learn how to sort data, where they can organize data in a way that makes it easier to find the information they need and to see relationships between different pieces of data
• Filtering
  Here, students will learn how to filter data, a powerful way in data analysis where they can easily view subsets of their data by hiding the rows that don't meet their criteria.
• IF ELSE
  The IF-ELSE function in Excel is a handy tool that allows us to perform different actions depending on whether a condition is met or not. This can be particularly useful when we have a large dataset and want to perform different analyses depending on specific criteria.

This module will cover the basic concepts of descriptive statistics, including measures of central tendency (mean, median, and mode) and measures of dispersion (range, variance, and standard deviation).

• Seeing patterns in the data
  Here, students will learn how to identify and analyze patterns in the data with the assistance of descriptive statistics.
• Sample and Population
  Here, students will gain an understanding of several concepts in probability, such as sample and population.
• Central Tendency (Mean, Median, Mode)
  This topic will make students familiar with measures of central tendency (mean, median, and mode) to help them calculate the average, find the median value of a dataset, and find the most frequent value.
• Dispersion (Range, Variance, Standard Deviation)
  This topic will make students familiar with measures of dispersion (range, variance, and standard deviation), which is essential for analyzing data sets because it can give us insights into the spread of the data.
• Five point Summary
  In this topic, students will understand the five-point summary in descriptive statistics.

Industry 4.0 is the term used to describe the fourth industrial revolution, and data is the lifeblood of Industry 4.0. In this module, students will explore the world of data and how data is critical for the industrial revolution.

• Introduction to Analytics Lifecycle
  In this chapter, students will go through the various phases involved in the data analytics lifecycle.
• Datasources and Databases
  Datasources are the information repositories that hold the data sets that analysts utilize to perform their work.
• A typical Data Pipeline
  This chapter will familiarize students with the data pipeline, a series of steps to ingest, transform and analyze raw data.
• Insight generation and Recommendation
  Here, students will familiarize themselves with the process of analyzing data to discover trends and patterns that can be used to generate new insights and make recommendations.
• End-to-end Business Case Study Demo
  Here, students will go through a hands-on demo of an end-to-end business case study.

In this module, students will explore the process of generating insights in multiple ways using Excel, such as tables, tabs, charts, and descriptive statistics.

• Pivot Tables
  This topic will make students understand pivot tables, which allow them to quickly summarize large amounts of data in a concise, easy-to-understand format.
• Sorting Data in Pivot Tables
  Here, students will learn how to sort data in pivot tables, where they can sort by values, by column, by row, and by multiple columns and rows.
• Filtering Data in Pivot Tables
  Here, students will learn how to filter data in pivot tables, where they can filter by date, product, customer, or any other entity.
• Analyse Tab
  Here, students will learn how to work with the analyze tab in Excel, which allows them to perform various statistical analyses on their data, like calculating means, standard deviations, percentiles, etc. They can also use the tab to create charts and graphs to visualize their data.
• Exploring charts
  In this topic, students will explore a variety of charts available in Excel to visualize data sets in multiple formats.
• Descriptive Statistics
  This chapter will help students analyze and understand diverse data sets in Excel with the aid of descriptive statistics.

This comprehensive guide will cover everything students need to know about storyboarding with Tableau, from the basics to advanced tips and tricks.

• Tableau Public Installation
  Tableau Public is a free data visualization software that can be installed on any computer to create interactive visualizations of data. Here, students will explore the process of installing Tableau Public on their systems.
• Dimensions & Measures
  In Tableau, dimensions are the qualitative data elements in your data set, while measures are the quantitative data elements. In this topic, we will discuss how to use both dimensions and measures in Tableau to perform various data analysis tasks.
• Data Types
  This topic will cover the implementation of several data types available in Tableau, such as string, date, time, numerical, boolean, geographic, and clusters.
• Choosing-charts w/ SHOW ME
  This topic will teach students to use the "SHOW ME" tool, which provides a quick way to create various charts based on their selected data.
• Calculations
  Calculations in Tableau are an essential part of data analysis. Creating calculated fields allows us to analyze data in ways that would not be possible with the raw data alone. This topic will show us how to create and use calculated fields in Tableau.
• Dates and Date Functions
  Here, students will learn about dates and date functions, which allow users to create visual representations of data over time, track changes in data over time, and identify trends.
• Filtering
  Filtering data in Tableau is a way of isolating data points within a larger dataset that meet the specific criteria you define. There are a few different ways to filter data in Tableau, which we will discuss in this topic.
• Dashboarding-101
  This topic covers everything from the basics of setting up your Tableau dashboard to more advanced topics like creating custom visualizations and using filters to manipulate your data.

Students will learn how to create stunning visualizations that tell a story and engage their audience. We'll also show students how to use Tableau's powerful features to bring their data to life.

• Parameters
  Here, students will learn how to filter data using parameters in Tableau.
• Actions
  Here, students will learn how to work with an action, an interactive element that can be used to filter and highlight data on a dashboard.
• Sorting
  This topic will familiarize students with a way to organize their data in a specific order with the aid of sorting.
• Special Charts
  In this topic, we'll explore some of the special charts that Tableau can create to communicate information effectively.
• What-if-analysis
  What-if-analysis in Tableau is the process of exploring data to find answers to questions you didn't know you had. Students will learn about this powerful process to gain insights into their data and make better decisions.
• Reshaping Data
  In this topic, we'll show students how to reshape data in Tableau and how to use the various features and tools available to make the process as easy and efficient as possible.
• Level-of-detail
  In this topic, we will learn about the level-of-detail in Tableau. Level-of-detail allows us to control the level of detail that is displayed in our charts and graphs.

Once students are done with all the fundamentals of data analytics, this data analytics essentials program will provide students with the third hands-on project on the topics learned so far.

Querying data with SQL allows us to find and manipulate data in our database quickly. In this module, students will learn how to write and understand SQL queries to retrieve data from any database.

• MySQL Installation
  Here, students will understand the process of installing MySQL (a free and open-source database management system) on their systems.
• Importing a Database
  Here, students will learn the process of importing a database into MySQL.
• Introduction to RDBMS
  This topic will introduce students to RDBMS, a relational database management system to create, store, update, and delete data in a relational database.
• Selecting data
  When working with data stored in a MySQL database, it is often necessary to select specific data in order to work with it. Here, students will learn how to select data in a variety of ways using the SELECT statement.
• Filtering data
  When working with databases, it is often necessary to filter data to return only the rows that meet specific criteria. Here, students will learn how to filter data and make their queries more specific using the WHERE clause.

Advanced querying encompasses a variety of techniques that allow a user to manipulate data in order to answer complex business questions. In this module, students will learn the process of advanced querying to extract business insights.

• Aggregating data
  Students will get familiar with data aggregation in SQL, a process of combining data from multiple tables into a single table, where a calculation is performed on a set of values and returns a single value.
• Joining data
  Students will familiarize themselves with combining data from two or more tables into a single table using the JOIN command.
• Window Functions
  Here, students will learn how to identify values in a collection of rows and provide a single result for each row, which is called the window function.
• Order-of-Execution
  Students will be introduced to the order-of-execution technique, which defines the specific order in which the clauses, expressions, and operators in a statement are evaluated.
• Extracting data to Excel to perform data analysis

Once students are done with the fundamentals of data analytics, this data analysis course for beginners will provide students with the first hands-on project on the topics learned so far.

This module will give students a comprehensive introduction to the Python programming language, covering topics like Google Colab, variables, data types, data structures, conditional statements, loops, and functions.

• Setting up Google Colab
  Google Colab is a free notebook environment for writing and executing code. Students will learn how to set up and work with Google Colab in this section.
• Variables
  Here, students will learn how to work with variables in Python to store values and retrieve them later.
• Data Types
  Here, students will understand data types, which define the type of data that a variable can hold. There are several built-in data types in Python, including integers, floats, and strings, among others.
• Data Structures
  Python's standard library provides a wide range of data structures that can be used to store and efficiently organize data. The most commonly used data structures are lists, tuples, dictionaries, and sets.
• Conditional Statements
  This topic will familiarize students with conditional statements that help them execute the code only if the specified condition is met.
• Loops
  The concept of loops will be taught to the students in this chapter. Loops can execute a block of code continually until a specific condition is met, such as computing the sum of two integers or displaying multiplication or other tables, among other things.
• Functions
  This chapter will help students understand and use Functions using Python programming so that they may reuse code.

Numpy is a powerful library for performing numerical operations on arrays and matrices. At the same time, Pandas is a library for working with data frames, which are similar to tables in a relational database. In this module, we'll explore how to use these two libraries to perform various data transformation tasks.

• Numpy Arrays
  A Numpy array is a multidimensional array of objects of the same type, and this topic will teach students how to perform numerical operations efficiently using Numpy arrays.
• Numpy Functions
  This article will make students familiar with various Numpy functions that can assist them in speeding up their code.
• Indexing
  Students will learn how to find and retrieve data from a given data structure using Indexing in this topic.
• Accessing
  Here, students will learn how to access data from a Python project using the dot (.) operator.
• Pandas Series
  In this topic, students will understand how to hold several data types, such as numbers, strings, etc., using a one-dimensional array-like object, i.e., the Pandas Series.
• Pandas Dataframes
  Here, students will gain an understanding of Pandas Dataframes, which are two-dimensional, size-mutable, potentially heterogeneous tabular data structures with labeled axes (rows and columns).
• Saving Loading
  Here, students will explore the process of saving and loading files in multiple formats using the Pandas library.
• Merging dataframes
  This topic will familiarize students with the process of combining/merging two or more dataframes into a single dataframe with the help of specific methods.
• Pandas Functions
  This topic will familiarize students with various Pandas functions that are widely implemented in numerous applications of data science and machine learning.

Exploratory Data Analysis, also known as EDA, uses visual techniques to help us find patterns and insights frequently inside specific data. This module will explain EDA using Python in-depth.

• Data Sanity Checks
  This topic will make students understand the significance of performing sanity checks to ensure that the data is clean and ready for analysis while working with data.
• Univariate Analysis
  The students in this topic will gain an understanding of how to perform statistical comparisons using univariate analysis.
• Bivariate Analysis
  The students in this topic will gain an understanding of how to perform statistical comparisons using bivariate analysis.
• Missing Value Treatment
  This topic will familiarize students with the number of ways to deal with missing values when performing exploratory data analysis.
• Outlier Detection
  This topic will familiarize students with the number of ways to detect outliers that can help identify problems and patterns in data for further analysis.

Seaborn is a powerful data visualization library that makes creating beautiful, informative visualizations easy. This module will teach students how to use Seaborn to create sophisticated visualizations, including histograms, line plots, joint charts, heatmaps, and more.

• Histogram
  In this topic, students will learn how to represent the distribution of numerical data in a graphical format using histograms accurately.
• Box Plot
  A box plot is a graph made up of a box and a whisker that shows the distribution of a data set. Here, students will get familiar with the process of showing the spread of the data and finding outliers.
• Line Plot
  Line plots are an excellent way to visualize relationships between numeric variables. Seaborn makes it easy to create high-quality line plots with just a few lines of code.
• Scatter Plot
  A scatter plot is used to indicate the data as a collection of points. Here, students will understand how Seaborn makes it easy to create a scatter plot for exploring and visualizing data.
• Joint Plot
  A joint plot is an excellent way to visualize the relationship between two variables. Here, students will learn about Seaborn's jointplot() function, which makes it easy to create these plots.
• Violin Plot
  Violin plots play the same roles as box plots and whisker plots. They show the distribution of a quantitative variable for several levels of a categorical variable and are beneficial for comparing distributions between different groups.
• Strip Plot
  A strip plot is a graphical representation of categorical data where a separate strip on the plot represents each category. This topic will make students understand strip plots' implementation using Seaborn to visualize the distribution of categorical data.
• Heatmap
  A heatmap is a graphical representation of data where the individual values contained in a matrix are represented as colors. This topic will make students understand the usage of seaborn.heatmap() function, which takes in a rectangular dataset and an optional argument for specifying the color palette.
• Plotly
  Plotly is a powerful Python library that allows you to create interactive, publication-quality figures. This topic will teach students how to work with Plotly, which helps create line plots, bar plots, scatter plots, and more.
• Customizing Plots
  This comprehensive guide will show students how to tweak every aspect of their Seaborn plots to create the perfect visualization for their data. By the end of this guide, students will be experts at creating beautiful, informative Seaborn plots that tell their data's story perfectly.

Once students are done with the data analysis essentials, this data analyst course for beginners will provide students with the second hands-on project on the topics learned so far.

• Overview of ChatGPT and OpenAI
• Timeline of NLP and Generative AI
• Frameworks for understanding ChatGPT and Generative AI
• Implications for work, business, and education
• Output modalities and limitations
• Business roles to leverage ChatGPT
• Prompt engineering for fine-tuning outputs
• Practical demonstration and bonus section on RLHF