Overview
Data Science Certification
The Data Science course enables you to understand practical foundations, helping you effectively execute and take up Big Data and other analytics projects. The program covers topics from Big Data to the Data Analytics Life Cycle. Understanding these topics helps in addressing business challenges that leverage Big Data.
Another aspect of this course is that it covers basic as well as advanced analytic methods, and also introduces the participant to Big Data technologies with tools like MapR and Hadoop. Our state-of-the-art-infrastructure allows students to understand the applications of these methods and tools by getting hands-on experience working alongside real-time data scientists. This program has an open approach including a final lab session, which explains various Big Data Analytics challenges by applying the concepts covered during the program with respect to the Data Analytics Life Cycle.
Who can learn Data Science?
The course is designed for anyone who wishes to understand the concepts of Data Science from a Data Scientist’s perspective. Professionals who can benefit from this course include:
Managers from any field, as Analytics is the best tool for managers these days
Business Analysts and Data Analysts who wish to upscale their Data Analytics skills
Database professionals who aspire to venture into the field of Big Data by acquiring analytics skills
Fresh graduates who wish to make a career in the field of Big Data or Data Science
What You'll Learn
- Be a part of a data science team and work on Big Data and various other analytics projects
- Deploy the Data Analytics Life Cycle for Big Data projects
- Change the frame of a challenge from a business perspective to analytics
- Understand which analytics techniques and tools will work in a specific Big Data analysis
- Create statistical models and understand which insights can lead to actionable results
- Select appropriate data visualizations, which would help in communicating analytics insights to business sponsors and analytics audience in a clearer manner
- Use various Big Data tools like Hadoop, MapR, R, In-Database Analytics, and MADLib functions
- Understand how to leverage advanced analytics to create a competitive advantage, and how the roles of data scientists and BI analysts are different from each other
Curriculum
- What is Data Science?
- Skill set required
- Job opportunities
- Continuous vs. Categorical variables
- Mean, Median, Mode, Standard Deviation, Quartile, IQR
- Hypothesis testing, z-test, t-test
Installation of R Studio
- Overview of R Studio components
- Data Structures
- Vector
- List
- Matrices
- Data Frame
- Factor
- Slicing and Sub-setting
- Vector
- List
- Matrix
- Data Frame
Functions in R
- In-built functions
- User-defined functions
Loops in R
- while
- for
- break
- next
Data Import in R
Apply Family of Functions
- lapply
- sapply
- tapply
Data Manipulation Using dplyr
Data Visualization Using ggplot2
What is Machine Learning?
Supervised vs. Unsupervised Learning
Exploratory Data Analysis
- Univariate analysis
- Boxplot
- Bivariate analysis
- Scatterplot
- Correlation
- Outliers
- Remove duplication
- Missing value imputation
Underfitting vs. Overfitting
Linear Regression
- Simple
- Multiple
- Assumptions of Linear Regression
- Evaluating Accuracy of model: k-Fold Cross validation
Logistic Regression
- Confusion Matrix
- ROC Curve
Time Series Forecasting
- Moving Average
- Exponential smoothing
- Holt Winter’s
- ARIMA
- Naïve Bayes
- Support Vector Machine
- K-Nearest Neighbor
- Decision tree
- Random Forest
- K-Means Clustering
- Introduction to Big Data
- Overview of Hadoop & its Ecosystem
- Introduction to NoSQL
- Overview of Apache Spark
Prerequisites
The following skill sets and knowledge will enable students to complete the course successfully, and at the same time, reap maximum benefits:
- Good understanding of basic statistical concepts and a strong quantitative background
- Knowledge of any scripting languages such as Java, Perl, Python, or R, as most of the modules in the course use R – an open-source statistical tool, and programming language.
- Knowledge and experience of SQL
Knowledge of these pre-requisites will enable the participants to understand various advanced tools and methods covered during the program more effectively.
FAQs
