Bhupen

Years of Experience in Big Data Training

10

Years of Experience in MongoDB Training

20

Teaching Experience in detail in MongoDB Training

Overall 24 years in IT industry. Led large size s/w development projects.

Years of Experience in Cassandra Classes

10

Years of Experience in Data Science Classes

18

Teaching Experience in detail in Data Science Classes

Lesson 1—Data Science Overview Know what Data Science is Discuss the roles and responsibilities of a Data Scientist List various applications of Data Science Understand how Data Science and Big Data work together Explore Data Science as a discipline What You’llLearn Understand how and why Data Science is gaining importance Understand what Python is and what problems it resolves Lesson 2 –Data Analytics Overview Data Analytics process and its steps Skills and tools required for Data Analysis Challenges of the Data Analytics Process Data visualization techniques Hypothesis testing to analyze data Exploratory Data Analysis technique (EDA) Lesson 3 –Statistical Analysis and Business Applications The difference between statistical and non-statistical analysis The statistical analysis process Mean, median, mode, and percentile Hypothesis testing and the Chi square test Types of frequencies Data distribution and the various methods of representing it The two major categories of statistical analysis and their differences Correlation matrix and its uses Lesson 04—Python: Environment Setup and Essentials How to install Anaconda and Jupyter notebook Some of the important data types supported by Python Data structures such as lists, tuples, sets, and dicts Slicing and accessing the four data structures Few basic operators and functions What You’ll Learn Some important control flow statements Lesson 5—Mathematical Computing with Python (NumPy) What NumPy is and why it is important Basics of NumPy, including its fundamental objects Create and print a NumPy array Use shape manipulation and copying methods Execute linear algebraic functions Carry out basic operations in NumPy Lesson 06—Python: Scientific computing with Python (SciPy) Why SciPy is needed The characteristics of SciPy The sub-packages of SciPy What You’ll Learn SciPy Sub-packages such as Optimization, Integration, Linear Algebra, Statistics, Weave, and IO Lesson 7—Data Manipulation with Pandas Pandas and its features Creating Series and DataFrame with data inputs Learning how to handle missing values Handling vectorized operations Viewing, selecting, and accessing elements in a data structure Analyzing data with different data operation methods Lesson 8—Machine Learning with Scikit–Learn What machine learningis and why it is important The machine learning approach Relevant terminologies that help you understand a dataset Features of supervised and unsupervised learning models Algorithms such as regression, classification, clustering, and dimensionality reduction Lesson 9 —Natural Language Processing (NLP) with SciKit Learn What is Natural Language Processing How Natural Language Processing is helpful Modules to load content and category Applying feature extraction techniques