Bootcamp Program with Dedicated Placement Assistance

The course commences with Excel. You will learn about the Data Wrangling process, which is all about the process of converting data from one form into another so that it becomes more organized, usable, and valuable.

Understand the meaning of Data Visualization and how it is done using Excel. Data Visualization is the representation of data in a graphical format that makes it easier to understand. It can be done using different tools like Tableau, Google Charts, etc. Here, you will learn how to do it using Excel, which provides different graphical formats, including Pie charts, Bar charts, Line charts, and much more.

This topic will let you explore Excel as a tool that solves data problems for businesses. Get a demonstration of Excel's essential functions by solving several problems in different areas, be it financial, economic, or mathematical.

This topic will introduce you to a relational database, which stores data in tables. Tables are similar to folders in a file system, where each table stores a collection of information. In a relational database, tables are related to each other by keys, and Keys are used to identify and link data in different tables.

Get aware of different commands used in SQL programming. SQL uses specific commands to Create, Drop, Insert, and more that help users carry out different tasks. These commands are mainly categorized into Data Definition Language (DDL), DQL (Data Query Language), DML (Data Manipulation Language), Transaction Control Language (TCL), and DCL (Data Control Language).

Get introduced to the PostgreSQL window function, along with many other inbuilt window functions for making calculations on data. You will also be introduced to SQL Statements, SQL Clauses, Operators, and Keywords.

Indulge in Joins, SQL subqueries, and temp tables, the methods for writing the queries to create tables. You will be taught how to work with Joins and subqueries. You will also get knowledge of temp tables, which are the materialized versions of the temporary data stores used in various use cases.

Here, you will get familiar with the foundational concepts of Python programming, including data types, strings, lists, tuples, loops, functions, and many more.

This topic will familiarize you with several data types in Python, including integers, floats, strings, booleans, lists, tuples, and dictionaries. Each data type has a specific purpose and is used to store different types of data.

Get aware of the ‘string’ data type in Python, which is usually a piece of text you want to display or "string" together.

You will learn to implement different conditional statements in Python, such as ‘if’ statements and loops.

Lists are mutable ordered sequences of objects, whereas tuples are immutable ordered collections of objects. They facilitate you to store and manipulate data. Here, you will understand the functioning of lists and tuples, along with their differences.

Here, you will learn how to execute a block of code repeatedly using the ‘for’ and ‘while’ loops.

A dictionary in Python is a collection of key-value pairs, and the keys in a dictionary must be unique and of a hashable data type. Sets are powerful data structures that allow you to store and manipulate data in many ways. In this topic, you will understand the functioning of dictionaries and sets, along with their differences.

Here, you will learn how to work with functions in Python, which are a block of code that runs and produces an output only when it is called.

Here, learners will get familiar with the functioning of one of the most powerful and widely used libraries in Python, Pandas. It provides a wide variety of features, including dataframe creation, manipulation, calculation, statistical analysis, plotting, and more. Pandas is a must-have tool for any Data Scientist or Data Analyst aspirant.

You will be introduced to dataframes, which are two-dimensional data structures where each column can contain a different data type. Each row in a dataframe represents a single observation, and each column represents a feature of that observation. Dataframes are a powerful tool for Data Analysis and Machine Learning.

In this topic, you will learn how to sort dataframes by column values, subset dataframes by row or column values, and create new columns. Python's Pandas package makes working with dataframes easy and efficient.

The Pandas library provides several powerful functions for dataframe aggregation. With these functions, you can quickly generate summary statistics for your dataframes, group dataframes by one or more columns, or create pivot tables.

In this topic, you’ll explore the different ways to slice and subset dataframes in Python. The topic will cover how to select specific columns, rows, and cells, as well as how to perform more complex subsets using Boolean indexing. With these methods in your toolkit, you’ll be able to tackle any subsetting task that comes your way.

Python provides a variety of options for visualizing data. You will explore several libraries in Python, such as matplotlib, seaborn, and plotly, which allow for creating intricate plots and visualizations. Python's visualization building blocks make it a powerful tool for Data Analysis and Exploration.

In this topic, you will learn how to create various types of plots using Python. You will learn how to create line plots, scatter plots, bar plots, and many more.

In this topic, you’ll take a look at three of the most popular Python visualization libraries: seaborn, matplotlib, and plotly. Each library has its own pros and cons; hence, you will learn how to create stunning visualizations using all these three libraries.

This sub-module will guide you through the process of conducting Exploratory Data Analysis (EDA), an essential step in any Data Analytics project, as it helps you understand the data better and identify any potential issues.

Data Quality Assurance and Pre-processing are integral to any Data Analysis project, especially when working with large and complex datasets. Here, you will discover a variety of techniques for Data Quality Assurance and Pre-processing, such as data cleaning, feature selection, and dimensionality reduction. These techniques can remove noise from data, identify and correct errors, and select the most relevant features for EDA, ensuring that data is of high quality and adequately processed.

Univariate profiling is an essential tool in Exploratory Data Analysis (EDA) for understanding the characteristics of individual variables in a dataset. You will understand how to identify patterns, understand relationships, and detect anomalies using univariate profiling in order to guide further analysis.

Multivariate profiling is the process of characterizing the relationships between multiple variables. You will understand how to identify relationships between variables, characterize the overall structure of the data, and detect outliers and unusual patterns using multivariate profiling. It is a powerful tool for understanding complex data sets.

Discover how to generate insights and report them in EDA, which is the process of taking a dataset and turning it into valuable information that can be used to improve decision-making. This process begins with data cleaning and preparation, followed by exploratory analysis to identify patterns and relationships.

This sub-module will provide you with a broad introduction to Machine Learning and its applications. You will learn about the foundations of Machine Learning, including Supervised and Unsupervised Learning, and will explore a variety of practical applications.

This topic will introduce you to the basics of Supervised Learning, an essential part of many Machine Learning applications. You'll learn about various Supervised Learning algorithms and explore how to evaluate and improve the performance of these algorithms.

This topic will introduce you to the basics of Unsupervised Learning, including what it is, how it works, and some of its applications. After this topic, you will learn to apply Unsupervised Learning to real-world problems.

Here, you will gain an understanding of SkLearn, a powerful Machine Learning library that provides a range of algorithms and tools for Data Mining and Data Analysis. It is written in Python and is designed to be easy to use and extend. SkLearn is well-suited for various tasks, including regression, classification, and clustering.

You will go through a step-by-step process of importing a dataset and connecting it to a data source with the help of Tableau.

Measures are numerical values, such as sales or profit, which can be aggregated. Dimensions are categorical values, such as product name or salesperson, which are used to group data. Calculated fields are new fields that are created based on calculations, such as ratios or percentages. Here, you will gain familiarity with these methods to quickly create new fields of data to understand the present information better.

You will discover the process of implementing multiple charts using Tableau, such as map charts, bar charts, line charts, and more, in order to visualize data quickly and gain insights to make better decisions.

In this topic, you’ll learn how to connect to different types of data, work with multiple data sources, and create basic charts and graphs. You'll also learn how to use Tableau’s powerful visualization features to create interactive dashboards and tell stories with your data.

Gain an understanding of joins and actions in Tableau, which enable you to combine data from multiple data sources and then perform actions on that data. For example, you could join data from sales and customer databases and then create an action that filters the data by customer name.