Data Science Training Institute in Gurgaon

What is data science?

Data science mean the study of large number of data which was stored on different databases or other medium. In this we record, analyse, cleaning and storing the data warehouse like Hive Apache. The goal of data science is to get result from structured and unstructured data.

What is the role of data scientist?

Data scientist knows the exact meaning of data. They collect data from various scores and store it on warehouse. After they clean and filter data by using languages and tools. They get accurate or predictive result of data with the help of statistics and other tools.

Will data scientist require coding?

Yes, Data scientist clean data with the help of Python and R Programming Languages

Is data science learning being hard?

Yes, Data science learning is hard. You must have known the basic concept of python and R Programming to learn data science training in Gurgaon. The student must have good knowledge of Statistics and python basic coding to learn data science.

What are the most important tools used in Data Science Course in Gurgaon?

Here are the few best tools which are used in data science course in Gurgaon. Few of them are : –

SAS
Apache Spark
BigML
Matlab
Excel
GGPLOT
Tableau
Jupyter
Matplotlib
Pandas
Scipy
Numpy
BeatuifulSoap
Socket
Tensor Flow
NLTK

Data Science : Syllabus

Data Science using Python

Difference Between Data analytics & Data Science?
Why and How python used in Data Analytics and Data Science?
What is Cubes – OLAP Framework in Python
What is Warehousing and DBMS
Data Analytics vs. Data warehousing, OLAP, Extract Transform Load (ETL), MIS Reporting
What are the problems and business objectives in different industries?
How leading companies are using the power of analytics?
Critical success Factor for AI
What are the analytics tools & their popularity?
What are Analytics Methodology & its framework?
What are Analytics projects?

Python Core Concept

What is Python
Introduction to installation of Python
Overview of Python Editors & IDE’s(Rodeo, Canopy, Jupyter , pycharm, python)
How to work on Jupiter Notebook and customize settings.
Basic Syntax, Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
Conditional Statement (If, Else-If or Nested If Else If)
Variable & Labels – Date & Time Values
Looping with Python (For Loop, While Loop or Nested Loop)
Control Statement in Python (Break Continue or Pass)
String Manipulation in Python: – Basic Operators, Method, Function etc.
Function and Modules (Importing, Installation, Packages with Version handling, Composition)
Input and Output Module in Python
Exception Handling in Python
OOPs Concept in Python
Classes, Inheritance, Overloading, Overriding, Data Hiding in Python.
Regular Expression in Python : – Match, Search, Modifier or Pattern
CGI Concept in Python : – Get and Post Method, CGI Enviroment Variable, Cookies, Upload, Get and Post Method
Database Handling in Python :- Connections, Transitions, Execution and Error Handling in Python
Networking: – Socket, Method, Internet Modules
Multithreading in Python : – Thread, Multithreaded
GUI Programming: – Tkinter and Widgets
Important Packages of Python : – NumPy, SciPy, Seaborn, scikit-learn, Pandas, Matplotlib, etc
Reading and writing data
Simple Graph plotting
Debugging & Code profiling

Python Syllabus and Upcoming Batches

Python & Advance Python Web Development with Python

Scientific Distribution

Numpy, Scify, Seaborn, Pandas, Scikitlearn, Statmodels, Nltk, MetplotLib etc

How to Import and Export Data by using python modules

How to Import Data from different sources?
Connection setup with database
Viewing Data objects – sub setting, methods
Export Data to different formats
Most Important python modules: Pandas, beautiful soup

Data Manipulation in Python and its Techniques

What is Manipulation in Python.
What are important Modules to Manipulate Data?
Cleaning and Prepping Data using Python.
Detecting Missing Values using python during cleaning.
What are Map and Data Library?
Data Manipulation steps: – Sorting, filtering, merging, duplicates, appending, sub setting, sampling, derived variables, Data type conversions, renaming, formatting etc)
Data manipulation Tools : – Functions, Operators, Packages, Control Structures, Loops, Arrays, Method etc
Python Built-in Functions
Python User Defined Functions and Classes.
How to stripping out extraneous information
Normalizing and Formatting data
Important Python modules for data.

Perform Data Visualization in Python.

What is EDA (Exploratory Data Analysis) in Python?
Descriptive statistics, Frequency Tables and summarizing Data.
Univariate and Multivariate Analysis (Data Distribution & Graphical Analysis)
Bivariate Analysis in Statistics (Distributions & Relationships, Cross Tabs, Graphical Analysis)
How we Plot Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density…
Important Packages for Exploratory Analysis (NumPy Arrays, Scipy, Matplotlib, seaborn, and Pandas etc)

Statistics using Python

What is Statistics in Python. How we calculate Mean, Median, Mode, Central Tendencies and R-Squared or Adj R-Squared using python.
Probability Distribution in python, Normal Distribution and Central Theorem using Python.
Statistics Concept and Hypothesis Concept of Testing
What is KS Test or Z Test . How to Calculate P Value using Distribution?
What is Anova, Correlations and Chi-square
Important modules used for statistical methods: Numpy, Scipy, Pandas

Predictive Analysis using python framework.

What is model in analytics and how it is used?
What are the best algorithm for prediction?
What is Data Modeling in Python?
Common Techniques used for analytics & modeling process
Most Popular modeling algorithms
Phases of Predictive Modeling

Data Exploration

What is Data Exploration and why it important?
EDA Methods and How it used in Machine Learning.
Common EDA framework for exploring the data and identifying problems with the data.
How to identify missing data?
How to identify outlier’s data?
Method to visualize the data trends and patterns.

Data Preparation

Data Preparation for Machine Learning.
Consolidate and Aggregation of Missing Values, Dummy Creation, Variable Reduction, Outlier Treatment.
Variable Reduction, Data Reduction Techniques, Principal Component Analysis, Improve Accuracy.

Data Exploration for Modeling

Need for structured exploratory data
EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
Identify missing data
Identify outlier’s data
Visualize the data trends and patterns

Segmentation in Machine Learning

What is Segmentation?
Edge Based Segmentation, Image Based Segmentation or Region Based Segmentation.
Different types of Segmentation (Subjective vs Objective, Heuristic vs. Statistical)
Heuristic Segmentation Techniques.
Behavioral Segmentation Techniques (K-Means Cluster Analysis)
Cluster evaluation and profiling
Implementation on new data

Linear Regression using Python

Introduction & Assumptions on Linear Regression
How to build Linear Regression Model
Standard metrics (R-Square/Adjusted R-Square, Global hypothesis, Variable significance, etc.)
Framework to assess the overall effectiveness of the model
Statistical Model Validation (Re running Vs. Scoring)
Business Outputs (Error distribution (histogram), Decile Analysis, Model equation, drivers etc.)
Interpretation of the Results, Business Validation and New Data Implementation

Logistic Regression for Machine Learning

What is Logistic Regression?
Difference between Linear Regression Vs. Generalized Linear Models Vs. Logistic Regression.
Build Logistic Regression Model (Binary Logistic Model)
Understand standard model metrics (Variable significance, Concordance, Gini, KS, Misclassification, Hosmer Lemeshov Test, ROC Curve etc)
Validation of Logistic Regression Models.
Standard Business Outputs (Lift charts, Model equation, Decile Analysis, ROC Curve, Probability Cut-offs, Drivers or variable importance, etc)
Interpretation of the Results, Business Validation and New Data Implementation

Time Series Forecasting

Intro About Time Series Forecasting
Components in Time Series – Seasonality, Trend, Cyclicity, Systematic, Level and Decomposition
Classification of Techniques (Pattern based or Pattern less)
Basic Techniques for Forecasting : – Averages, Smoothening, etc
Advanced Techniques – AR Models, holt’s winter, holt’s linear, ARIMA, etc
Measure Forecasting Accuracy – MAPE, MAD, MSE, etc

Machine Learning

Machine Learning Vs Predictive Modeling
Types of Business problems – Cache Mapping of Techniques – Regression vs. segmentation vs. classification vs. Forecasting.
Essentials Classes of Learning Algorithms -Supervised Vs Unsupervised Learning
What are different Phases of Predictive Modeling (Data Pre-processing, Model Building, Sampling, Validation)
Overfitting & Performance Metrics
Feature engineering & dimensionality reduction
Cost function Optimization
Overview of gradient descent algorithms.
What is Cross validation (Bootstrapping, K-Fold validation etc)
Overview on Model performance metrics (R-square, Adjusted R-squre, precision, sensitivity, specificity, RMSE, MAPE, AUC, ROC curve, recall, confusion metrics )

Decision Trees in Machine Learning

Introduction on Decision Trees.
Types of the Decision Tree Algorithms
Construct of Decision Trees using Simplified Examples
Generalizing Decision Trees
Pruning a Decision Tree
Decision Trees with Validation
Overfitting is best practice.

Supervised Learning: Ensemble Trees

Ensembling in Machine Learning
Difference between Manual Ensembling Vs. Automated Ensembling
Methods of Ensembling.
Bagging Boosting, Stacking and Random Forest (Logic, Practical Applications)
Ada Boost
Gradient Boosting Machines (GBM)
XGBoost

Unsupervised Learning

Importance of segmentation & Role of ML in Segmentation?
Distance in Math – Formulas and Concept.
K-Means Clustering algorithm
Expectation Maximization algorithm
Hierarchical Cluster Analysis
Sklearn Clustering (DBSCAN)
Principle components Analysis (PCA)

ANN (Artificial Neural Network)

Neural Networks modification and Its Applications
Single Layer Neural Network (Perceptrons), and Hand Calculations
Learning In a Multi Layered Neural Net.
Deep neural Networks for Regression
Deep neural Networks for Classification
Interpretation of Outputs and Fine tune the models with Hyper Parameters Tunning
Validating Artificial Neural Network models