Certification Course in Data Science from Bangalore

Why Data Science Course?

Data Scientist is a person who Usually analyses entire accumulated data from various sources, across various machines concerned with the subject or a product in order to provide suitable insights to his company. The growth of the company depends upon the strategically plans drawn Particularly by forecasting marketing changes depending upon the insights provided by their data science.

To summarize Dial Now 8147111254 for any queries regarding data science course fee & Job Assistance

Who can take Data Science Certification Course in Bangalore?

• IT professionals
• Business Analysts
• Data Analysts
• Warehouse managers
• Application Developers
• Job seekers in the IT industry
• Business operators

Data Science Training Modules

Module 1: Python Essentials

Learning Objective: To understand why Python is a useful scripting language for developers. To learn how to design and program Python applications.

Introduction

• What is Python..?
• A Brief history of Python
• Why Should I learn Python..?
• Installing Python
• How to execute Python program
• Write your first program

Variables & Data Types

• Variables
• Numbers
• String
• Lists, Tuples & Dictionary

Conditional Statements & Loops

• If…statement
• If…else statement
• elif…statement
• The while…Loop
• The for….Loop

Control Statements

• Continue statement
• Break statement
• Pass statement

Functions

• Define function
• Calling a function
• Function arguments
• Built-in functions

Modules & Packages

• Modules
• How to import a module…?
• Packages
• How to create packages
• Classes & Objects

Introduction about classes & objects

• Creating a class & object
• Inheritance
• Methods Overriding
• Data hiding

Files & Exception Handling

• Writing data to a file
• Reading data from a file
• Read and Write data from CSV file
• Try…except
• Try…except…else
• Finally
• OS module

Module 2: Statistics

Learning Objective: Demonstrate knowledge of statistical data analysis techniques utilized in decision making. Apply principles of Data Science to the analysis of problems. Be able to restate an investigative question in terms of a statistical model or algorithm.

Inferential statistics

• Standard error
• Confidence interval
• Hypothesis testing
• p-value

Sample vs. population mean

• Variance of population

Probability and statistics

• Probability density functions
• Poisson Process
• Law of large numbers
• Normal distribution
• Sampling distribution
• Mean
• Variance of Bernoulli
• Regression

Module 3: Introduction to Machine Learning (ML)

Learning Objective: The purpose of machine learning is to discover patterns in your data and then make predictions based on those often, complex patterns to answer business questions, and help solve problems. Machine learning helps analyze your data and identify trends.

Introduction

• What is Machine learning?
• Overview about sci-kit learn and tensorflow
• Types of ML
• Some complementing fields of ML
• ML algorithms
• Machine learning examples

NumPy Arrays

• Creating multidimensional array
• NumPy-Data types
• Array attributes
• Indexing and Slicing
• Creating array views and copies
• Manipulating array shapes
• I/O with NumPy

Working with Pandas

• Installing pandas
• Pandas data frames
• Pandas Series
• Data aggregation with Pandas Data Frames
• Concatenating and appending Data Frames
• Joining Data Frames
• Handling missing data

Python Regular Expressions

• What are regular expressions?
• The match Function
• The search Function
• Matching vs searching
• Search and Replace
• Extended Regular Expressions
• Wildcard

Python Oracle Database Access

• Install the cx_Oracle and other Packages
• Create Database Connection
• CREATE, INSERT, READ, UPDATE and DELETE Operation
• DML and DDL Operation with Databases
• Performing Transactions
• Handling Database Errors
• Disconnecting Database

Regression based learning

• Simple regression
• Multiple regression
• Logistic regression
• Predicting house prices with regression

Clustering based learning

• Definition
• Types of clustering
• The k-means clustering algorithm

Data mining

• Introducing data mining
• Decision Tree
• Affiity Analysis
• Clustering

Introducing matplotlib

• Bar Charts
• Line Charts
• Scatter plots
• Bubble charts

Working with openCV

• Setting up opencv
• Loading and displaying images
• Applying image filters
• Tracking faces
• Face recognition

Module 4: Performing predictions with Linear Regression

Learning Objective: The basic objective of this course is to learn How to calculate a simple linear regression step-by-step, How to perform all of the calculations using a spreadsheet, How to make predictions on new data using your model, A shortcut that greatly simplifies the calculation.

Simple linear regression

• Multiple regression
• Training and testing model

Introduction to Python

• Installation of Python framework and packages: Anaconda & pip Writing/
• Running python programs using Spyder Command Prompt Working with Jupyter
  Notebooks.
• Creating Python variables
• Numeric, string and logical operations
• Data containers: Lists, Dictionaries, Tuples & sets Practice assignment

Iterative Operations & Functions in Python

• Writing for loops in Python
• While loops and conditional blocks List/Dictionary comprehensions with loops
• Writing your own functions in Python Writing your own classes and functions

Data Handling in Python using NumPy & Pandas

• Introduction to NumPy arrays, functions & properties Introduction to Pandas &
  Data frames
• Importing and exporting external data in Python Feature engineering using
  Python

Data Science & Machine Learning in Python

• Converting business problems to data problems
• Understanding supervised and unsupervised learning with examples
• Understanding biases associated with any machine learning algorithm Ways of
• Reducing bias and increasing generalization capabilities Drivers of machine
• Learning algorithms
• Cost functions
• Brief introduction to gradient descent
• Importance of model validation
• Methods of model validation
• Cross validation & average error

Generalized Linear Models in Python

• Linear Regression
• Regularization of Generalized Linear Models
• Ridge and Lasso Regression
• Logistic Regression
• Methods of threshold determination and performance measures for Classification score models

Tree Models using Python

• Introduction to decision trees
• Tuning tree size with cross validation Introduction to bagging algorithm Random
  Forests
• Grid search and randomized grid search ExtraTrees (Extremely Randomised
  Trees) Partial dependence plots
• Support Vector Machines (SVM) & kNN in Python
• Introduction to idea of observation based learning Distances and similarities
  k Nearest Neighbors (kNN) for classification Brief mathematical background on
  SVM Regression with kNN & SVM

Unsupervised learning in Python

• Need for dimensionality reduction
• Principal Component Analysis (PCA) Difference between PCAs and Latent
• Factors Factor Analysis
• Hierarchical, K-means & DBSCAN Clustering

Text Mining in Python

• Gathering text data using web scraping with urllib
• Processing raw web data with BeautifulSoup
• Interacting with Google search using urllib with custom user agent collecting twitter data with
• Twitter API
• Naive Bayes Algorithm
• Feature Engineering with text data
• Sentiment analysis