100% FREE.ALL COURSES.UNLIMITED ACCESS.

Module 1

Data Science Fundamentals

This module is optimized to help you develop a strong foundation for a data science career. It dives deep into the core principles of probability, statistics, and mathematics necessary for building machine and deep learning models further in the program. You will start working with data and learn how to visually present the results of your analyses.

01Advanced Microsoft Excel32 lessons

02Data Analysis with Excel Pivot Tables19 lessons

03Data Literacy58 lessons

04Data Strategy81 lessons

05How to Think Like a Data Scientist to Become One21 lessons

06Introduction to Data and Data Science22 lessons

07Introduction to Excel83 lessons

08Introduction to Tableau40 lessons

09Mathematics12 lessons

10Power BI76 lessons

11Probability47 lessons

12SQL for Data Science Interviews23 lessons

13Starting a Career in Data Science: Project Portfolio, Resume, and Interview Process52 lessons

14Statistics44 lessons

Module 2

Programming for Data Science

Here you will focus on developing a versatile programming skillset. You will acquire a thorough functional understanding of relational databases, using essential SQL queries to preprocess data, as well as coding and leveraging popular libraries in Python, like NumPy and matplotlib. You will also learn the best ways to manipulate and visualize data in R.

01Data Cleaning and Preprocessing with pandas27 lessons

02Data Preprocessing with NumPy68 lessons

03Dates and Times in Python23 lessons

04Git and GitHub12 lessons

05Introduction to Jupyter9 lessons

06Introduction to Python41 lessons

07Introduction to R Programming87 lessons

08Python Programmer Bootcamp128 lessons

09SQL121 lessons

10SQL + Tableau20 lessons

11The Complete Data Visualization Course with Python, R, Tableau, and Excel100 lessons

Module 3

Machine & Deep Learning

Building on the foundations developed in the first 2 modules, Module 3 will teach you how to apply advanced statistical methods to execute predictive analytics. You will learn how to use sklearn to build complete linear and logistic regression models from scratch, and how to cluster unlabeled datasets with k-means. You will then progress to building deep learning models with the Keras library in TensorFlow 2.0, optimizing your neural network with backpropagation and fine-tuning your model.

01Convolutional Neural Networks with TensorFlow in Python48 lessons

02Deep Learning with TensorFlow 283 lessons

03Linear Algebra and Feature Selection32 lessons

04Machine Learning in Excel70 lessons

05Machine Learning in Python72 lessons

06Machine Learning with Decision Trees and Random Forests20 lessons

07Machine Learning with K-Nearest Neighbors17 lessons

08Machine Learning with Naïve Bayes14 lessons

09Machine Learning with Ridge and Lasso Regression19 lessons

10Machine Learning with Support Vector Machines15 lessons

11SQL + Tableau + Python61 lessons

Module 4

Advanced Specialization

By now, you will have developed a solid understanding of Python programming and statistical modeling. The concluding module gives you the opportunity to mold your data science expertise according to a field of your choosing. From developing next-gen fintech products to helping retail giants boost profitability through customer analytics, you’ll be able to make a valuable contribution to a diverse industry spectrum.

01A/B Testing in Python27 lessons

02AI Applications for Business Success27 lessons

03Credit Risk Modeling in Python58 lessons

04Customer Analytics in Python60 lessons

05Data-Driven Business Growth38 lessons

06Fashion Analytics with Tableau41 lessons

07Introduction to Business Analytics54 lessons

08Product Management for AI & Data Science67 lessons

09Python for Finance65 lessons

10Time Series Analysis with Python89 lessons

11Web Scraping and API Fundamentals in Python48 lessons

……………………..AND MANY MORE………………..

190+Hours of video

610+Exercises

LEARN FREE : 365 DATA SCIENCE

5 days later

it is free for limited time or life time? i guess its for limited time..

    a year later

    plz any one downloaded resources files plz upload for courses…