Introduction to Data Science

We will begin with the introduction to Data Science and cover all the fundamentals like Programming, Statistics, Probability, etc. which are the key topics in Data Analytics course.

• Python Programming
  Programming is needed to implement the concepts learnt for Machine Learning and Data Science. In this module, you will learn to start from the basics of programming and then getting introduced to python. Further, in the module you will learn how to write python programs for Data Science.

• Descriptive Statistics
  Descriptive Statistics is a factual technique in Data Science that sums up and depicts the information utilizing mean, middle, and so forth, in tables and graphs. In this module, you will figure out how to utilize this technique to comprehend information substantially clearer.

• Introduction to Probability
  Probability is a numerical device used to examine irregularity, like the chance of an occasion happening in an arbitrary test. In this module, you will find out about how to calculate Probability which is utilized in Business Analytics.

• Probability Distributions
  A statistical function reporting the chances that a random variable lies within a range is known as Probability Distribution. This module will show you Probability Distributions and different sorts like Binomial, Poisson, and Normal Distribution.

• Hypothesis Testing and Estimation
  We all do experiments and build our observations. Hypothesis testing and Estimation is a necessary step to follow in Applied Statistics for doing experiments based on the observed/sample Data. This module helps you to learn about Hypothesis Testing and Estimations.

• Sample Tests and Inferences
  Samples under observation or under tests can be of different types. Could be possible, we just want to check one result, or compare two different sample, check proportions, or check for multiple samples. This module helps you to learn about z-Test, t-Test, ANOVA, Chi-Sq

SQL Programming

We will covering the basics of table, understanding tables and data in it. Queries to fetch data, DBMS Concepts, Normalization , Joins, etc.

• Introduction to DBMS
  Database Management Systems (DBMS) is a software storage system which stores data. You can edit and organize the data in the storage system. Storages in the storage system are known as Databases. This module will teach you everything you need to know about DBMS from the Data Science perspective.

• ER-Diagram
  An Entity-Relationship(ER) diagram is a blue print that portrays the relationship among entities and their attributes. The module will teach you how to make an ER diagram using several entities and their attributes.

• Schema Design
  Schema design is a schema diagram that specifies the name of record type, data type, and other constraints lke primary key, foreign key, etc. It is a logical view of the entire database.

• Key Constraints and Basics of Normalization
  Key constraints are used for uniquely identifying an entity within its entity set, in which you have a primary key, foreign key, etc. Normalization is one of the essential concepts of DBMS, which is used for organizing data to avoid data redundancy. In this module, you will learn how and where to use all key constraints and normalization basics.

• Joins
  As the name implies, a join is an operation that combines or joins data or rows from other tables based on the common fields amongst them. In this module, you will go through the types of joins and learn how to combine data.

• Subqueries
  This module will teach you how to work with subqueries/commands that involve joins and aggregations. You will also learn about independent subqueries and correlated subqueries in the module.

• Sorting
  As the name proposes, Sorting is a strategy to organize the records in a particular request for a reasonable comprehension of announced information. This module will show you how to sort information in any order like ascending or descending.

• Groups and Filtering
  Grouping is a feature in SQL that arranges the same values into groups using some functions like SUM, AVG, etc. Filtering is a powerful SQL technique, which is used for filtering and specifying a subset of data that matches criteria. This is very frequently used in Data Understanding. In this module, you will learn how to use these analytical functions.

Advanced Statistics

• Regression Analysis
  Regression Analysis is a statistical technique used to analyse the relationship between a dependent variable and one or more independent variables. In this module, you will learn several variations like linear regression, multiple linear, and non-linear regression.

• Dimension Reduction Techniques
  Dimension Reduction transforms data from a high dimensional to low dimensional space without losing any vital information. This module will teach you how to work with various Dimension Reduction techniques in Machine Learning.

Introduction to Supervised and Unsupervised Learning
Supervised and Unsupervised learning techniques are one of the essential learning algorithms in Machine Learning. Supervised learning models are trained using labelled data, whereas unsupervised learning models are trained using unlabelled data.

Clustering
Clustering is an unsupervised learning technique involving the grouping of data. In this module, you will learn everything you need to know about the method and its types like K-means clustering, hierarchical clustering, DBScan, etc.

Decision Trees
Decision Tree is a Supervised Machine Learning algorithm used for both classification and regression problems. It is a hierarchical structure where internal nodes indicate the dataset features, branches represent the decision rules, and each leaf node indicates the result.

Random Forest
Random Forest is a popular supervised learning algorithm in machine learning. As the name indicates, it comprises several decision trees on the provided dataset’s several subsets. Then, it calculates the average for enhancing the dataset’s predictive accuracy.

Neural Networks
A Neural Network is a computing system in deep learning based on the biological neural network that makes up the human brain. In this module, you will learn all the neural networks’ applications.

Multiple Linear Regression
Multiple Linear Regression is a supervised machine learning algorithm involving multiple data variables for analysis. It is used for predicting one dependent variable using various independent variables. This module will drive you through all the concepts of Multiple Linear Regression used in Machine Learning.

Logistic Regression
Multiple Linear Regression is a supervised machine learning algorithm involving multiple data variables for analysis. It is used for predicting one dependent variable using various independent variables. This module will drive you through all the concepts of Multiple Linear Regression used in Machine Learning.

Linear Discriminant Analysis
Linear Discriminant Analysis (LDA) is a dimension reduction technique used for building machine learning models. This module will drive you through all the concepts of LDA used in supervised machine learning.

Introduction to Time Series
Time-Series Analysis comprises methods for analysing data on time-series to extract meaningful statistics and other relevant information. Time-Series forecasting is used to predict future values based on previously observed values.

Correlation and Forecasting
In this module, you will learn how to collect data and predict the future value of data focusing on its unique trends. This technique is known as Forecasting data.

Auto-Regressive Models
The autoregressive model uses regressed data from the previous time series to predict future data at the next time series.

Machine Learning Algorithms
This module will drive you through all the Machine Learning algorithms’ concepts and use them for training your models. This also helps you understand how to get the results of the model and check for the model performances if they are performing as per the standards.

Bias – Variance Trade-off
Not every data is good and so are the models. A model can never be 100 percent accurate in real life situations. Hence there will be an error associated with it always. Bias-variance trade-off helps to understand how to handle this situation.

Handling Unbalanced Data
Unbalanced Data or Imbalanced data is where the data is not categorised clearly which is a major challenge in the industry today. In this module, you will learn how to train your model on imbalanced data.

Boosting
As the name suggests, Boosting is a meta-algorithm in machine learning that converts robust classifiers from several weak classifiers. Boosting can be further classified as Gradient boosting and ADA boosting or Adaptive boosting. One of the most commonly used algorithms from GB family is XGB also known as Extreme Gradient Boosting.

Model Validation and Deployment
This module will teach you which model best suits architecture by evaluating every individual model based on the requirements. You will also learn how to deploy a model once you have decided on the model from a list of models built.

Technical Reports
The module will tell you about how the technical reports need to be published for Data Science models and how to show case your analysis to your client/stakeholders.

Business Reports
It is not always the technical part which a Data Scientist needs to do. He should be a good story teller too. In this module, you will learn how to tell your technical story in business terms and how to present it to the business side of your client/stakeholders.