Master In Data Science

Course Overview

Target Audience for Introduction to Data Science Certification Training

• Undergraduate and graduate students interested in Data Science
• IT professionals seeking a career change
• Business Analysts wanting to understand data usage
• Researchers dealing with large data sets
• Individuals interested in data analytics and visualization
• Marketing professionals needing data-driven decision making.

Course Curriculum

Lesson 1 - Introduction

Lesson 2 - Sample or population data?

Lesson 3 - The fundamentals of descriptive statistics

Lesson 4 - Measures of central tendency, asymmetry, and variability

Lesson 5 - Practical example: descriptive statistics

Lesson 6 - Distributions

Lesson 7 - Estimators and estimates

Lesson 8 - Confidence intervals: advanced topics

Lesson 9 - Practical example: inferential statistics

Lesson 10 - Hypothesis testing: Introduction

Lesson 11 - Hypothesis testing: Let’s start testing!

Lesson 12 - Practical example: hypothesis testing

Lesson 13 - The fundamentals of regression analysis

Lesson 14 - Subtleties of regression analysis

Lesson 15 - Assumptions for linear regression analysis

Lesson 16 - Dealing with categorical data

Lesson 17 - Practical example: regression analysis

R Programming for Data Science

Gain insight into the R Programming language with this introductory course. An essential programming language for data analysis, R Programming is a fundamental key to becoming a successful Data Science professional. In this course you will learn how to write R code, learn about R’s data structures, and create your own functions. After the completion of this course, you will be fully able to begin your first data analysis. Key Learning Objectives Learn about math, variables, and strings, vectors, factors, and vector operations Gain fundamental knowledge on arrays and matrices, lists, and data frames Get understanding on conditions and loops, functions in R, objects, classes, and debugging Learn how to accurately read text, CSV and Excel files plus how to write and save data objects in R to a file Understand and work on strings and dates in R.

Course Curriculum

Lesson 1 - R basics

Lesson 2 - Data structures in R

Lesson 3 - R Programming fundamentals

Lesson 4 - Working with Data in R

Lesson 5 - Stings and Dates in R

Data Science with R The next step to a data scientist is learning R - the upcoming and most in-demand open source technology. R is an extremely powerful Data Science and analytics language which has a steep learning curve and a very vibrant community. This is why it is quickly becoming the technology of choice for organizations who are adopting the power of analytics for competitive advantage. Key Learning Objectives Gain a foundational understanding of business analytics Install R, R-studio, and workspace setup, and learn about the various R packages. Master R programming and understand how various statements are executed in R. Gain an in-depth understanding of data structure used in R and learn to import/export data in R. Define, understand and use the various apply functions and DPYR functions. Understand and use the various graphics in R for data visualization. Gain a basic understanding of various statistical concepts. Understand and use hypothesis testing method to drive business decisions. Understand and use linear, non-linear regression models, and classification techniques for data analysis. Learn and use the various association rules and Apriori algorithm. Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering.

Course curriculum

Lesson 1: Introduction to Business Analytics

Lesson 2 - Introduction to R Programming

Lesson 3 - Data Structures

Lesson 4 - Data Visualization

Lesson 5 - Statistics for Data Science-I

Lesson 6 - Statistics for Data Science-II

Lesson 7 - Regression Analysis

Lesson 8 - Classification

Lesson 9 - Clustering

Lesson 10 – Association

Python for Data Science Kickstart your learning of Python for Data Science with this introductory course and familiarize yourself with programming. Carefully crafted by IBM, upon completion of this course you will be able to write your Python scripts, perform fundamental hands-on data analysis using the Jupyter based lab environment, and create your own Data Science projects using IBM Watson. Key Learning Objectives Write your first Python program by implementing concepts of variables, strings, functions, loops, conditions Understand the nuances of lists, sets, dictionaries, conditions and branching, objects and classes Work with data in Python such as reading and writing files, loading, working, and saving data with Pandas

Course curriculum

Lesson 1 - Python Basics

Lesson 2 - Python Data Structures

Lesson 3 - Python Programming Fundamentals

Lesson 4 - Working with Data in Python

Lesson 5 - Working with NumPy arrays

Data Science with Python This Data Science with Python course will establish your mastery of Data Science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and gain in-depth knowledge in data analytics, Machine Learning, data visualization, web scraping, and natural language processing. Python is a required skill for many Data Science positions, so jump start your career with this interactive, hands-on course. Key Learning Objectives Gain an in-depth understanding of Data Science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics Install the required Python environment and other auxiliary tools and libraries Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions Perform high-level mathematical computing using the NumPy package and its vast library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave Perform data analysis and manipulation using data structures and tools provided in the Pandas package Gain expertise in Machine Learning using the Scikit-Learn package Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline

Use the Scikit-Learn package for natural language processing Use the matplotlib library of Python for data visualization Extract useful data from websites by performing web scraping using Python Integrate Python with Hadoop, Spark, and MapReduce

Course Curriculum

Lesson 1 - Data Science Overview

Lesson 2: Data Analytics Overview

Lesson 3: Statistical Analysis and Business Applications

Lesson 4: Python Environment Setup and Essentials

Lesson 5: Mathematical Computing with Python (NumPy)

Lesson 6 - Scientific computing with Python (Scipy)

Lesson 7 - Data Manipulation with Pandas

Lesson 8 - Machine Learning with Scikit–Learn

Lesson 9 - Natural Language Processing with Scikit Learn

Lesson 10 - Data Visualization in Python using matplotlib This lesson teaches you to visualize data in python using matplotlib and plot them.

Lesson 11 - Web Scraping with BeautifulSoup

Lesson 12 - Python integration with Hadoop MapReduce and Spark

Machine Learning Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques, including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced Machine Learning knowledge. Key Learning Objectives Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises Acquire thorough knowledge of the statistical and heuristic aspects of Machine Learning Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python Validate Machine Learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which include Boosting & Bagging techniques Comprehend the theoretical concepts and how they relate to the practical aspects of Machine Learning

Course Curriculum

Lesson 1 - Introduction to Artificial Intelligence and Machine Learning

Lesson 2: Data Wrangling and Manipulation

Lesson 3: Supervised Learning

Lesson 4: Feature Engineering

Lesson 5: Supervised Learning-Classification

Lesson 6: Unsupervised learning

Lesson 7: Time Series Modelling

Lesson 8: Ensemble Learning

Lesson 9: Recommender Systems

Lesson 10: Text Mining

Tableau Desktop 10 This Tableau Desktop 10 training will help you master the various aspects of the program and gain skills such as building visualization, organizing data, and designing dashboards. You will also learn concepts of statistics, mapping, and data connection. It is an essential asset to those wishing to succeed in Data Science. Key Learning Objectives Grasp the concepts of Tableau Desktop 10, become proficient with statistics and build interactive dashboards Master data sources and datable blending, create data extracts and organize and format data Master arithmetic, logical, table and LOD calculations and ad-hoc analytics Become an expert on visualization techniques such as heat map, tree map, waterfall, Pareto, Gantt chart and market basket analysis Learn to analyze data using Tableau Desktop as well as clustering and forecasting techniques Gain command of mapping concepts such as custom geocoding and radial selections Master Special Field Types and Tableau Generated Fields and the process of creating and using parameters Learn how to build interactive dashboards, story interfaces and how to share your work.

Course Curriculum

Lesson 1 - PGetting Started With Tableau

Lesson 2 - Working With Tableau

Lesson 3 - Deep diving with Data and Connections

Lesson 4 - Creating Charts

Lesson 5 - Adding calculations to your workbook

Lesson 6 - Mapping data in Tableau

Lesson 7 - Dashboards and Stories

Lesson 8 - Visualizations For an Audience

Big Data Hadoop and Spark Developer Learn how to work Big Data and its components. Deep-dive into Hadoop and its ecosystem including MapReduce, HDFS, Yarn, HBase, Impala, Sqoop and Flume. This course provides an introduction to Apache Spark which is a next step after Hadoop. After completing this course, you will be able to successfully pass the Cloudera CCA175 certification but embrace this technology as part of your role as a Data Scientist. Key Learning Objectives Master the concepts of the Hadoop framework and its deployment in a cluster environment Understand how the Hadoop ecosystem fits in with the data processing lifecycle Learn to write complex MapReduce programs Describe how to ingest data using Sqoop and Flume Get introduced to Apache Spark and its components List the best practices for data storage Explain how to model structured data as tables with Impala and Hive

Course curriculum

Lesson 1 - Introduction to Big Data and Hadoop Ecosystem

Lesson 2 - HDFS and Hadoop Architecture

Lesson 3 - MapReduce and Sqoop

Lesson 4 - Basics of Impala and Hive Lesson 5 - Working with Hive and Impala

Lesson 6 - Type of Data Formats

Lesson 7 - Advanced HIVE concept and Data File Partitioning

Lesson 8 - Apache Flume and HBase

Lesson 9 - Apache Pig

Lesson 10 - Basics of Apache Spark

Lesson 11 - RDDs in Spark

Lesson 12 - Implementation of Spark Applications

Lesson 13 - Spark Parallel Processing

Lesson 14 - Spark RDD Optimization Techniques

Lesson 15 - Spark Algorithm

Lesson 16 - Spark SQL

Data Science Capstone Project This Data Science Capstone project will give you an opportunity to implement the skills you learned throughout this Program. Through dedicated mentoring sessions, you’ll learn how to solve a real-world, industry-aligned Data Science problem, from data processing and model building to reporting your business results and insights. The project is the final step in the learning path and will enable you to showcase your expertise in Data Science to future employers. Key Learning Objectives Data Science Capstone course will bring you through the Data Science decision cycle, including data processing, building a model and representing results. The project milestones are as follows: Data Processing - In this step, you will apply various data processing techniques to make raw data meaningful. Model Building - You will leverage techniques such as regression and decision trees to build Machine Learning models that enable accurate and intelligent predictions. You may explore Python, R or SAS to build your model. You will follow the complete model-building exercise from data split to test and training and validating data using the k-fold cross-validation process. Model Fine-tuning - You will apply various techniques to improve the accuracy of your model and select the champion model that provides the best accuracy. Dashboarding and Representing Results - As the last step, you will be required to export your results into a dashboard with meaningful insights using Tableau