This is a complete Data Science bootcamp specialization training course that provides you detailed learning in data science, data analytics, project life cycle, data acquisition, analysis, statistical methods and machine learning. This course will prepare you for your Certified Analytics Professional (CAP®) designation.

In this course, you will learn how to apply Data Science through seven pragmatic steps – Frame, Acquire, Refine, Transform, Explore, Model, and Insight – to any business problem. The focus will be to learn the principles through an applied case study and by actually coding in Python to solve this.

• You will gain expertise to deploy Recommenders using Apache Mahout, data analysis, data transformation, experimentation and evaluation.
• Learn how to employ statistical and machine learning algorithms to solve real life problems by working on real time Projects.
• Develop proficiency in using python and its libraries like Pandas, numPy, Seaborn.

• Big Data Specialists, Business Analysts and Business Intelligence professionals
• Statisticians looking to improve their Big Data statistics skills
• Developers wanting to learn Machine Learning (ML) Techniques
• Information Architects looking to learn Predictive Analytics
• Those looking to take up the roles of Data Scientist and Machine Learning Expert
• Those who are wanting to achieve Certified Analytics professional certification

DataScience Course Content

Python Fundamentals

• Introduction To Python
• “Hello Python Program” in IDLE
• Jupyter Notebook Tutorial
• Spyder Tutorial
• Introduction to Python
• Variable, Operators, Data Types
• If Else, For and While Loops
• Functions
• Lambda Expression
• Filter, Map, Reduce
• Taking input from keyboard

NumPy

• Create Arrays
• Array Item Selection and Indexing
• Array Mathematics
• Array Operation

Pandas

• Introduction to Pandas
• Series
• Series indexing and Selection
• Series Operation
• Data Frames
• Data Collection from csv, json, html, excel
• Data Merging, Concatenation, Join
• Group By and Aggregate Function
• Order By
• Missing Value Treatment
• Outlier Detection and Removal
• Pandas builtin Data Visualisation

Visualisation- Matplotlib, Seaborn

• Line Plots
• Scatter Plots
• Pair Plots
• Histograms
• Heat Maps
• Bar Plots
• Count Plots
• Factor Plots
• Box Plots
• Violin Plots
• Swarm Plots
• Strip Plots
• Pandas Builtin Visualisation Library

Statistics

• Descriptive vs Inferential Statistics
• Mean, Median, Mode, Variance, Std.dev Scatter Plots
• Central Limit Theorem
• Co-Variance
• Pearson’s Product Moment Correlation
• R – Square
• Adjusted R-Square
• Spearman’s. Rank order Coefficient
• Sample vs Population
• Standardizing Data(Z-score)
• Hypothesis Testing
• Normal Distribution
• Bias Variance Tradeoff
• Skewness
• P Value
• Z-test vs T-test
• The F distribution
• The chi-Square test of Independence
• Type-1 and Type-2 errors

Introduction to Machine Learning

• Machine Learning Use Cases
• Supervised vs Unsupervised vs Semi-Supervised
• Machine Learning process Workflow
• Training a model
• Validating results
• Overfitting vs Underfitting
• Ordinal vs Nominal data
• Structured vs unstructured vs semi-structured data
• Intro to scikit Learn
• Supervised
• Unsupervised
• Ensemble
• NLP
• Deep learning

• Having 11+ years of experience in the Industry
• Working as a Senior Software Engineer
• 4 years of experience in DataScience