Data Analytics Essentials

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.

Here, students will learn the process of importing a database into MySQL.

This topic will introduce students to RDBMS, a relational database management system to create, store, update, and delete data in a relational database.

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.

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.

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.

Students will familiarize themselves with combining data from two or more tables into a single table using the JOIN command.

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.

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.

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.

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.

Here, students will learn how to work with variables in Python to store values and retrieve them later.

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.

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.

This topic will familiarize students with conditional statements that help them execute the code only if the specified condition is met.

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.

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.

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.

This article will make students familiar with various Numpy functions that can assist them in speeding up their code.

Students will learn how to find and retrieve data from a given data structure using Indexing in this topic.

Here, students will learn how to access data from a Python project using the dot (.) operator.

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.

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).

Here, students will explore the process of saving and loading files in multiple formats using the Pandas library.

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.

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.

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.

The students in this topic will gain an understanding of how to perform statistical comparisons using univariate analysis.

The students in this topic will gain an understanding of how to perform statistical comparisons using bivariate analysis.

This topic will familiarize students with the number of ways to deal with missing values when performing exploratory data analysis.

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.

In this topic, students will learn how to represent the distribution of numerical data in a graphical format using histograms accurately.

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 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.

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.

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 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.

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.

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 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.

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.

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

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.

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.

This topic will cover the implementation of several data types available in Tableau, such as string, date, time, numerical, boolean, geographic, and clusters.

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 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.

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 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.

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.

Here, students will learn how to filter data using parameters in Tableau.

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.

This topic will familiarize students with a way to organize their data in a specific order with the aid of sorting.

In this topic, we'll explore some of the special charts that Tableau can create to communicate information effectively.

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.

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.

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.