What is Data Analytics?

With the amount of data generated every day, Data Analytics is more important than ever. Having the ability to collect and analyze all this generated data, allows companies like google to have monopoly in the market. Another important thing about Data Data Analytics is that, it’s no longer limited to IT industry. It is used in almost every industry where data is generated, may it be health or marketing.

What you will learn in the course?

This course will teach you how to handle and analyze data using Python. We will begin by learning how to work with CSV files using the pandas library, including how to clean and format data. Next, we will explore how to perform similar tasks using SQL. Throughout the course, you will learn how to create visualizations to better understand your data using plotting techniques. Additionally, you will learn how to scrape data from the web, as data collection is an essential step in data science. By the end of the course, you will have a solid understanding of how to collect, clean, format, and analyze data using Python, preparing you for a career in data science.

Introduction to Jupyter notebooks: Jupyter notebooks are an interactive environment for writing and running code, as well as for creating and sharing documents that contain live code, equations, visualizations, and narrative text. In this course, we will be using Jupyter notebooks as our primary development environment.

Numpy: Numpy is a powerful library for working with arrays and matrices in Python. It provides a wide range of mathematical functions and tools for working with arrays, including indexing, broadcasting, and linear algebra operations. In this course, we will learn how to use Numpy to perform various array operations.

Indexing: Indexing is the process of selecting elements from an array or matrix based on their position. In this course, we will learn how to use Numpy to index and select elements from arrays using various techniques such as slicing, boolean indexing, and advanced indexing.

Broadcasting: Broadcasting is a technique used in Numpy to perform operations on arrays with different shapes. It allows us to perform mathematical operations on arrays of different sizes, as long as certain rules are followed. In this course, we will learn how to use broadcasting to perform operations on arrays of different shapes.

Pandas: Pandas is a powerful library for working with data in Python. It provides a wide range of data structures and tools for working with data, including Series and DataFrames. In this course, we will learn how to use Pandas to work with data, including loading, cleaning, formatting, and analyzing data.

Introduction: Pandas provides a wide range of data structures and tools for working with data, including Series and DataFrames. In this course, we will learn how to use Pandas to work with data, including loading, cleaning, formatting, and analyzing data.

Series and DataFrame: A Series is a one-dimensional array-like object that can hold any data type. A DataFrame is a two-dimensional table of data with rows and columns. In this course, we will learn how to create and manipulate Series and DataFrames using Pandas.

Slicing and Indexing: Slicing and indexing are used to select specific rows and columns from a DataFrame. In this course, we will learn how to use slicing and indexing to select specific rows and columns from a DataFrame.

Boolean Indexing: Boolean indexing is used to select rows from a DataFrame based on a certain condition. In this course, we will learn how to use boolean indexing to select rows from a DataFrame based on a certain condition.

Combining Tables: Combining tables is used to join two or more tables based on a common column. In this course, we will learn how to use Pandas to join two or more tables based on a common column.

Groupby and pivot tables: Groupby and pivot tables are used to group and summarize data in a DataFrame. In this course, we will learn how to use Pandas to group and summarize data in a DataFrame.

Data Visualization: Data visualization is the process of creating visual representations of data. It is a powerful way to communicate complex information and insights, and can help to identify patterns and trends in the data. In this course, we will learn how to use various data visualization techniques and tools to create effective and engaging visualizations.

Pandas: Pandas is a powerful library for working with data in Python. It provides a wide range of data structures and tools for working with data, including Series and DataFrames. In this course, we will learn how to use Pandas to work with data, including loading, cleaning, formatting, and analyzing data. We will also use pandas visualization functions to create different types of charts.

Seaborn: Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for creating visually attractive and informative statistical graphics. In this course, we will learn how to use Seaborn to create various types of data visualizations, including scatter plots, line plots, bar plots, and heatmaps.

Matplotlib: Matplotlib is a plotting library for Python. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK. In this course, we will learn how to use Matplotlib to create various types of data visualizations, including line plots, scatter plots, bar plots, and histograms.

SQL: SQL (Structured Query Language) is a programming language used for managing and manipulating relational databases. In this course, we will learn how to use SQL to perform various database operations such as creating tables, inserting data, querying data, and updating data.

Web scraping is a technique for extracting information from websites. It involves making HTTP requests to a website’s server, downloading the HTML of the web page, and then parsing that HTML to extract the data you’re interested in. Web scraping is a powerful tool for data collection and can be used for a wide range of applications, from price comparison to sentiment analysis. In this course, we will learn how to use Python and various libraries such as BeautifulSoup.

• Projects
  • 4x Intermediate Exercises
  • 3x Capstone Projects
  • 2x Interview Questions

By the course end . . .

You will have good command on libraries like numpy, pandas, and matplotlib. You will feel very comfortable working with data, both CSV files and SQL databases. Loading data, cleaning it, formatting it, and analyzing it will all feel intuitive. You will also know web scraping which you can use not only for data science but also for various fun projects. By the end of the course, you will have a solid understanding of how to collect, clean, format, and analyze data using Python. As a result, you will have a project portfolio that demonstrates your proficiency in data science and your ability to solve real-world problems. This portfolio will be an excellent tool for showcasing your skills to potential employers and can serve as an important aspect of your job search. It will also help to create job opportunities for you as the potential employers will be able to see your ability to work on real-world projects and your problem solving skills, making you a valuable asset to any organization looking to hire Data Analyst.