Top Data Science and Analytics Courses by Guvi

Data science and analytics are rapidly growing global fields with a strong job outlook. The Bureau of Labor Statistics projects that the employment of data scientists will grow by 36% from 2021 to 2031, much faster than the average growth rate of 5% for all occupations. One of the leading providers of data science and analytics courses in India is Guvi. This blog covers some of the best data science and analytics courses Guvi offers at different experience levels.

GUVI (Grab Ur Vernacular Imprint) is an IIT-M & IIM-A incubated Ed-tech company that provides personalised learning solutions for its learners from online learning, upskilling & recruitment opportunities in world-class quality. A highlight of this platform is that it offers courses in your native language as well.

Guvi's Data Science and Analytics courses are designed for beginners and experienced learners. These Guvi courses cover various topics, from Python programming basics to advanced machine learning algorithms. Industry experts teach them and include hands-on projects to help you apply your skills to real-world data.

Let's start!

1. Data Visualization in Python

Average Learner Rating - 4.58

Course Description - The Data Visualization in Python course covers some of Python’s most popular and robust data visualization libraries, such as Matplotlib, Seaborn, and Bokeh. It also teaches how to implement them on vast amounts of data. You will gain the skills needed to plot and present data more meaningfully. You will also learn to leverage tools such as Anaconda and Jupyter Notebook. After completing this course, you will be able to perform data visualization on real-world datasets.

Prerequisites

• Basic knowledge of Python.
• An interest in performing data visualization to draw meaningful insights from large amounts of data.

Course Outcome -

• Have a strong grasp of data visualization concepts with Python.
• Proficiency in working with Matplotlib and using it to visualize data.
• Learn how to use Seaborn and visualize data with various types of plots.
• Work with Bokeh and be able to spin up advanced annotations.
• Perform data visualization on vast amounts of data and real-world datasets.
• Efficiently use Python, Anaconda, and Jupyter Notebook tools for data visualization.

2. Data Analysis using Pandas

Average Learner Rating - 4.4

Course Description -

Data Analysis using Pandas course will help you learn how to handle various operations using Pandas, logical categorising, data manipulation, and statistical analysis. You will also be taught to create, deploy and navigate data frames while visualising, grouping, slicing, and indexing them. After completing this course, you will gain a globally recognised certificate and be able to work with almost any real-world dataset applications worldwide.

Prerequisites

• Basic knowledge of Python.
• An interest in data analysis techniques and how to implement them using Python.

Course Outcome -

• Learn how to implement data analysis leveraging Pandas.
• Learn how to combine, visualise, and apply logic to datasets.
• Work with and perform various operations with the Pandas library efficiently.
• Handle data-related problems such as erroneous or missing data.
• Learn how to create, deploy and navigate data frames.
• Perform functions like grouping, slicing, and indexing on large datasets.

3. Introduction to Data Science with R

Average Learner Rating - 4.59

Course Description -

Introduction to Data Science with R will teach you how to use R and various other tools to effectively analyse data from multiple sources, clean and transform data, perform data visualisation, analyse data, and draw meaningful insights. After this course, you can also implement machine-learning techniques and gain a globally recognised certificate.

Prerequisites

• Basic knowledge of data science and coding with R.
• Knowledge of a few statistical techniques.

Course Outcome -

After completing this course, you will be able to -

• Understand and implement data science fundamentals using R.
• Write and implement code in the R programming language.
• Learn about various statistical distributions and their importance.
• Perform data analysis with data from multiple sources and formats in R.
• Visualise data and draw meaningful insights from it.
• Work with various machine learning techniques such as supervised and unsupervised learning.

4. R Programming

Average Learner Rating - 4.46

Course Description -

Learn R programming and all of its core concepts with this beginner-friendly course. The course will discuss different data types in R and how to work with them, using loops and functions for efficient coding, effectively manipulating data for data analysis and modelling, and conducting data visualisation like a pro. 

Prerequisites

• Basic knowledge surrounding data and statistics.
• Interest in learning R and working with large amounts of data to drive intelligent solutions.

Course Outcome -

• Knowledge of R programming language and all its concepts.
• Write R code.
• Effectively conduct data manipulation using R.
• Learn how to work with data.
• Perform data visualisation and analysis on large amounts of data.
• Gain practical understanding by working on a real-world project.

5. Machine Learning

Average Learner Rating - 4.46

Course Description -

The Machine Learning course by Guvi will help you learn a series of ML concepts such as Supervised and Unsupervised learning, Classification, Regression, SVM’s decision trees, Overfitting, and Random forests. You will be working with one of the most popular ML libraries in Python, Scikit-learn, and will learn to write code like a pro. You will also build and train classification and regression models and evaluate their performance on real-world datasets.

Prerequisites

• Basic knowledge of the Python programming language.
• Beginner knowledge of Machine Learning can be helpful.

Course Outcome -

• Gain a deeper knowledge of Machine Learning concepts and how to implement them.
• Write Python code using Scikit-learn like a pro.
• Understand Supervised and Unsupervised Learning.
• Implement several different Machine Learning algorithms and monitor their output.
• Create prediction models using Classification and Regression.
• Build and train ML models and implement them on massive real-world datasets.

What Do Learners Say?

Here are some data science course reviews by the course takers, explaining how the Guvi courses helped them in upskilling and career growth. 

These are just a few of the many great Data Science and Analytics courses offered by Guvi. With so many options to choose from, you can find the perfect course to reach your data science goals.