# Data Science Course in Bangalore

Crampete's Data Science course in Bangalore stands out from other training classes in a few ways: the Data Science training is offered as an instructor-led classroom course, the syllabus includes a Python, Statistics, Regression, Anova, Exploratory Data Analysis, and Supervised Machine Learning; you will build a Data Science capstone project portfolio; and finally, we also provide you with job assistance. Read on to know more about this top Data Science training program in Bangalore.

This course, designed for both students with and without experience in Data Science Course in Bangalore, makes you a well-rounded professional.

### Course Syllabus

Modules are picked to give you a well-rounded ability in Data Science Course in Bangalore skills.

MODULE 1: PYTHON FOR DATA ANALYSIS

Get a basic overview of Jupyter Notebook and its components; and learn about the three most popular Python libraries: Numpy, Pandas and matplotlib.

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• 1. Environment Set-up
• 2. Jupyter Overview
• 3. Python Numpy
• 4. Python Pandas
• 5. Python Matplotlib
MODULE 2: INTRO TO STATISTICS

Statistics is an essential skill required to master data science. This module will introduce you to the most important statistical concepts that are used in data science.

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• 1. Difference between a population and a sample
• 2. Types of variables
• 3. Measures of Central Tendency
• 4. Measures of Variability
• 5. Coefficient of variance
• 6. Skewness and Kurtosis
MODULE 3: INFERENTIAL STATISTICS

Inferential statistics is a main branch of statistics. It allows you to make informed decisions based on the analysis of a representative sample of large data sets.

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• 1. Normal Distribution
• 2. Central Limit theorem
• 3. Confidence Interval
• 4. Student's T distribution
• 5. Test hypotheses
• 6. Type I and Type II errors
• 7. T-test
MODULE 4: REGRESSION AND ANOVA

Learn about a powerful statistical method that allows you to establish a relationship between two or more objects of interest. Also learn about the analysis of variance (ANOVA), which helps you analyse the differences among sample sets.

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• 1. Regression
• 2. Anova
• 3. Correlation and Causation
• 4. R square
• 5.Adjusted R square
MODULE 5: EXPLORATORY DATA ANALYSIS

Learn how to use visual methods to analyse data sets to summarize their main characteristics. See data beyond the formal modelling or hypothesis testing.

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• 1. Intro to Exploratory Data Analysis
• 2. Derive insights of Data with python pandas
• 3. Missing value analysis
• 4. Outlier detection analysis
• 5. The correlation matrix
MODULE 6: SUPERVISED MACHINE LEARNING

Learn how to use visual methods to analyse data sets to summarize their main characteristics. See data beyond the formal modelling or hypothesis testing.

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• 1. Intro to Supervised Machine Learning
• 2. Linear Regression with an example using python sci-kit learn module
• 3. Logistic Regression with an example using python sci-kit learn module
• 4. Naïve Bayes method and an example using python sci-kit learn module
• 5. Decision Tree classifier and an example using python sci-kit learn module
• 6. Random forest classifier and an example using python sci-kit learn module
• 7. Support vector machine (SVM) and an example using python sci-kit learn module
• 8. Intro to Neural network

### Programming Languages / Tools you will learn

In this course, you'll get familiar and master the following tools. These tools will help you deliver the job in the best way possible.

### Projects

Each project is designed to deepen and solidify your theoretical and conceptual understanding.

Retail

Your team has been hired by the clothing company Columbus to produce a model to predict whether a customer will make a purchase or not on its website. To create the model, the company has made available data extracted from its website and from its Google Analytics system. The data can be used to understand the behavior of clients during their online visit. In similar projects, this information has proven to be helpful in understanding the customer's purchasing intention.

Real Estate

Estimating house prices is an essential task for buyers, sellers, and banks who will provide a loan. However, house prices may vary depending on the building, location, as well as market trend. Currently, a data collection of more than 30 thousand houses has been obtained, and your task is to analyze this data collection by conducting predictive modelling and visualization.

Web & Social Media

Tapping social media exchanges on Twitter- A case study to reveal people's sentiment, trend over the disaster period, the effectiveness of social media in disaster management.

Healthcare

You will be working with images of dermatology samples collected and classified by doctors. The goal of the machine learning task is to determine which type of ailment is contained in the sample.

Banking

Imagine you work for our fraud detection team in the Claim department as a modeller. Your task is to create a predictive model based on historical claim data. For this case competition, you are tasked with identifying first-party physical damage fraudulence and explaining reasons of fraudulent claims. The team is interested in the key drivers of fraudulence and wants to achieve an accuracy as high as possible.

### Delivery Methods

We offer convenient training methodologies to suit your individual learning styles and preferences.

### Batch Details

Flexible options that suit your learning temperament, to take where you want to be.

ONLINE LIVE

Sat Jan 06 2024

Admission Deadline: Thu Jan 04 2024

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CLASSROOM

Sat Jan 06 2024

Admission Deadline: Thu Jan 04 2024

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### A community in the making!

Here's what our students have to say about being a part of the Crampete family and learning skills that empower them! A community in the making- sharing skills, experience and knowledge!

"Most web development coaching is terrible. Crampete's free videos conveyed their mastery of the subject. Taking a leap of faith, I enrolled. In four months, I became really good at web development. A big Thanks To Justina mam, Dinesh sir and Crampete."