Data Science

The Data Science curriculum is designed in a way to help students gather knowledge in the field of business, besides applying the tools and statistics to meet organizational challenges shortly. Therefore, the skills acquired during the trajectory of Data Science and Data Analytic courses are indispensable to becoming an asset in the field of Data Science. Whether you are looking for Data Science Syllabus for beginners or experts, here we have curated a general syllabus of Data Science. Following are the 3 most important components of Data Science which are followed by most universities to help you adapt to both the theoretical and practical aspects of the subject:

Module 1: Python

Python is the most important and necessary topic that every data scientist should have knowledge about. In this section, our instructors will take you through the basics of Python and areas where it can be used. You will learn how to use some of the current tools such as Numpy, Pandas, and Matplotlib. Therefore, module 1 includes –

⦁ Environment set-up
⦁ Jupyter overview
⦁ Python Numpy
⦁ Python Pandas
⦁ Python Matplotlib

Module 2: R language

⦁ Used for statistical and data analysis, R programming language is one of the advanced statistical languages used in data science. This module teaches you how to explore data sets using R. Here you will learn –
⦁ An introduction to R
⦁ Data structures in R
⦁ Data visualization with R
⦁ Data analysis with R

Module 3: Statistics

When working with data, the knowledge of statistics is necessary and an important skill set that you must have. In this module, you will learn –

⦁ Important statistical concepts used in data science
⦁ Difference between population and sample
⦁ Types of variables
⦁ Measures of central tendency
⦁ Measures of variability
⦁ Coefficient of variance
⦁ Skewness and Kurtosis

Module 4: Inferential statistics

Inferential statistics is used to make generalizations of populations, from which samples are drawn. This is a new branch of statistics, which helps you learn to analyze representative samples of large data sets. In this module, you will learn –

⦁ Normal distribution
⦁ Test hypotheses
⦁ Central limit theorem
⦁ Confidence interval
⦁ T-test
⦁ Type I and II errors
⦁ Student’s T distribution

Module 5: Regression and Anova

This lesson will help you understand how to establish a relationship between two or more objects. ANOVA or analysis of variance is used to analyze the differences among sample sets. Here you will learn –

⦁ Regression
⦁ ANOVA
⦁ R square
⦁ Correlation and causation

Module 6: Exploratory data analysis
In this lesson, you will learn –

⦁ Data visualization
⦁ Missing value analysis
⦁ The correction matrix
⦁ Outlier detection analysis

Module 7: Supervised machine learning
This is a comprehensive module to help you understand how to make machines or computers interpret human language. You will learn –⦁ the Python Scikit tool

⦁ Neural networks
⦁ Support vector machine
⦁ Logistic and linear regression
⦁ Decision tree classifier

Module 8: Tableau
Tableau is a sophisticated business intelligence tool used for data visualization. In this lesson, you will learn –

⦁ Working with Tableau
⦁ Deep diving with data and connection
⦁ Creating charts
⦁ Mapping data in Tableau
⦁ Dashboards and stories

Module 9: Machine learning on cloud
In this lesson, you will learn –

⦁ ML on cloud platform
⦁ ML on AWS
⦁ ML on Microsoft Azure