Learn about Data Science—what it is, who works as a Data Scientist, and what can be achieved through the discipline.  

You will receive a thorough introduction to Python's data analytics tools and methods in this course. It will introduce data cleaning and manipulation techniques.

The abstraction of Series and DataFrame as the key data structures for data analysis will also be covered, as well as tutorials on how to effectively use group by, merge, and pivot tables.

Students will graduate with the ability to take tabular data, clean it, manipulate it, and perform fundamental statistical analyses.

Having advanced skills in Python is crucial for many data science roles. In this course, you will continue to build on the Python programming skills you acquired in the previous class by applying them to analytical projects. 

You will learn Python for data science together with concepts such as data wrangling, mathematical computing, and more using a blended learning approach.

Data Science with Python training can help you advance your career as a data scientist.            

In this course you will learn how to use SQL to store, query, and manipulate data.

SQL is a special-purpose programming language designed to manage data in a relational database and is used by many apps and organizations across the globe.

This course aids in your mastery of Tableau Desktop -a widely used corporate intelligence, reporting, and data visualization tool. With the help of our Tableau training, you may advance your analytics profession and acquire employable skills.

Employers value Tableau skills highly for data-related positions, and our course teaches you how to utilize the tool efficiently for data preparation, interactive dashboard creation, the addition of different dimensions, and in-depth analysis of outliers.

The mathematical underpinnings of machine learning are given at the start of this course. Before moving on to multivariate and logistic regression, it begins with a review of linear algebra and univariate linear regression.

Furthermore, it switches topics every week to cover a wide range of machine learning models and approaches. Deep learning, support-vector machines, and principal component analysis are a few of these.

Finally, it discusses relevant practical issues like how to plan and use significant machine learning initiatives. You'll have a thorough understanding of machine learning, its ideas, and its methodologies at the end of the course. In addition, you'll be able to use core machine learning algorithms like k-means clustering and back propagation.

You'll get the chance to put your newly acquired skills into practice through the project.

You will learn how to tackle a practical, industry-relevant Data Science problem through focused mentorship sessions, from data processing and model creation to reporting your business outcomes and insights.

The project is the last stage of data science training and will provide you an opportunity to practice it in a practical setting.