MaxMunus's Data Science Training will help you learn the Python programming which is the basis for Data Science and Machine Learning. This training will cover Data Science with Python and will also explian Machine Learning in deep. Apart from Python the course also covers Data Science elements like Introduction to Statistics and Probability using Python, Acquiring Data from various sources like CSV, text, API, Web scraping etc.
The course also covers in detail topics like Numpy and Pandas where it covers shape manipulation, n-dimensional array, Series and Dataframes, Time series, Visualization with Matplotlib -plotting etc.
The course also covers Data Wrangling where it covers Cleaning up the Data, Dimensionality Reduction etc.
In Machine Learning part the course covers Linear Regression, Naive Bayes, Decision trees, K-means clustering, Tokenizing, Stemming, Lemmatizing etc.
This course also gives you an opportunity to do a hands on project on Data Science where you will use all the skills learned during this course. This project will increase your technical skills and gives you a lot of confidence in executing any project.
Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!
Data Science Course Duration: 66 Hours
Data Science Training Timings: Week days 1-2 Hours per day (or) Weekends: 2-3 Hours per day
Data Science Training Method: Online/Classroom Training
Data Science Study Material: Soft Copy
Recommended but not mandatory:
Introduction to Python programming
Data Science and Machine Learning
What is Data science - Intro to Data Science
Introduction to Statistics and Probability using Python
Acquiring Data from various sources
Python packages for Data Science
Natural Language Processing
There are many datasets available. The following are very popular projects among the beginners. However, if you have a project that you want do, you will get support from us. In either case, you can either do it alone or in group.
1. Iris data set- predict the type of flower from the given dataset.
2. Load Prediction: determine whether a loan will default, as well as the loss incurred if it does default.
3. Walmart sales prediction:
a. Predict the sales across various departments in each store.
b. Predict the effect of markdowns on the sales during the holiday seasons.
4. Boston Housing: Predict the median value of occupied homes.
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