Python Course &
Workshop

As a Data Scientist, you are required to understand the business problem, design a data analysis strategy, collect and format the required data, apply algorithms or techniques using the correct tools, and make recommendations backed by data.

The program concludes with a capstone project designed to reinforce the learning by building a real industry product encompassing all the key aspects learned throughout the program. The skills focused on in this program will help prepare you for the role of a Data Scientist.

Tools Covered..
Flume, NumPy, pandas, SciPy, Spark, IBM Watson, Apache HBASE, hive, Pig, Sqoop,Hadoop Hdfs, Hadoop Map Reduce, Python, R, Scala


Detailed Program

1. Data science overview
2. Data Analytics Overview
3. Statistical Analysis & Business Application
4. Python Environment Setup & Essentials
5. Mathematical Computing with Python (NumPy)
6. Scientific Computing with Python (Scipy)
7. Data Manipulation with Pandas
8. Machine Learning with Scikit-Learn
9. Data Visulation in Python using matplotlib
10. Web Scraping with BeautifulSoup
11. Python integration with Hadoop MapReduce & Spark

An exciting branch of Artificial Intelligence, this Machine Learning certification online course will provide the skills you need to become a Machine Learning Engineer and unlock the power of this emerging field.
In-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised & unsupervised learning, regression, classification, and time series modeling.

Eligibility
Course is well-suited for participants at the intermediate level including, analytics managers, business analysts, information architects, developers looking to become data scientists, and graduates seeking a career in Data Science and Machine Learning.

Requires an understanding of basic statistics and mathematics at the college level, Familiarity with Python programming is also beneficial.
You should understand these fundamental courses including Python for Data Science, Math Refresher, and Statistics Essential for Data Science


Detailed Program

1. Introduction to AL & Machine Learning
2. Data Pre-processing
3. Supervised Learning
4. Feature Engineering
5. Supervised Learning Classification
6. Unsupervised Learning
7. Time Series Modeling
8. Ensemble Learning
9. Recommender Systems
10. Text Mining

This program focuses on the fundamental building blocks you will need to learn in order to become an AI practitioner. Specifically, you will learn programming skills, and essential math for building an AI architecture. You’ll even dive into neural networks and deep learning.

Course 1: Introduction to Python
Why Python Programming
Data Types and Operators
Control Flow
Functions
Scripting
Classes

Course 2: Anaconda, Jupyter Notebook, NumPy, Pandas, and Matplotlib
Anaconda
Jupyter Notebooks
NumPy Basics
Pandas Basics
Matplotlib Basics

Course 3: Linear Algebra Essentials
Vectors
Linear Combination
Linear Transformation and Matrices
Linear Algebra in Neural Networks
Labs

Course 4: Calculus Essentials
Derivatives Through Geometry
Chain Rule and Dot Product
More on Derivatives
Limits
Integration
Calculus in Neural Networks

Course 5: Neural Networks
Introduction to Neural Networks
Training Neural Networks
Deep Learning with PyTorch