Data Analytics Using Python Training In Chennai

What is Exploratory Data Analysis (EDA)?

Exploratory data analysis is a paramount part of Data Science which allows the analyst to make the decision of their data sets and establish idea using relationship between the data sets. Ultimately, EDA analysing the dataset with Visual and Statistical results.

Why Exploratory Data Analysis with Python?

For EDA techniques statistical graphs and data visualization are important for analyzing the data, So using Python packages like Numpy, pandas features are play critical role in Exploratory Data Analysis.

Brush up Python basics for your ultimate Exploratory data Analysis

What are Prerequisites are need to learn Exploratory Data Analysis Python Course?

Having knowledge on Python is helpful to quick learning. But if you don’t have knowledge on Python, then No worries about it. Our training program scratches basic level Python concepts to advanced Python packages.

Knowledge on Data Visualization and statistical would be added advantage.

In the current era of Data and Information Data Analytics play a major role in analyzing the raw data to conclude about the information it has using different software and systems. Data Analytics has currently been used in different domains to make more informed business decisions which help the organization to improve.

Data Analytics uses different processes and techniques like Business Intelligence, Online Analytics processing and advanced analytics to inspect the date and transform into useful information.

Course Features

Duration60 hours
Skill levelAll level
Batch Strength15
AssessmentsYes
Mock InterviewsYes
Resume BuildingYes
PlacementsYes
Flexible TimingYes
Fee InstallmentsYes
LanguageTamil/English

CHAPTER 1: Python – a brief tour

Why Python? (U.S.P of Python)
Python object model
Installing Python environment & package management
Interactive learning using Jupyter notebooks
Python data structures
Python control structures & statements
Useful Python built-in functions
Functional programming
Modules and namespaces
Useful Python built-in and third-party modules
Object oriented programming
Real world examples
Exercise

CHAPTER 2: Data Analysis

Importance of data in current scenario
Numpy arrays and vectorized computation
Broadcasting using Numpy arrays
Numpy ufuncs
Take a dive into pandas
Series, Dataframe and Index objects in pandas
Analysis of structured and semi-structured data using pandas
Data loading, storage and file formats
Generating insights from data
Data cleaning and preparation
Data wrangling: Join, combine and reshape
Plotting and visualization
Data aggregation and group operations
Time series
Advanced pandas features

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CHAPTER 3: Statistics

Introduction and Data collection
Descriptive statistics
- Measures of central tendency
- Variation and shape
- Sampling and population
- Empirical rule and Chebychev rule
- Covariance and Coefficient of Correlation
Probability
- Concepts of basic probability
- Conditional probability
- Bayes’ Theorem
Probability distributions
- Probability distribution for a discrete random variable
- Discrete distributions - Binomial and Poisson distribution
- Continuous distribution - Normal distribution
- Sampling and sampling distributions
Hypothesis testing
- Concepts
- Null & Alternative hypothesis
- Critical value of test statistics
- Regions of rejection and acceptance
- Critical value approach and p-value approach
- One-tail & two-tail tests
- Real world problems
Regression
- Linear regression
- Multiple linear regression
- Logistic regression
Decision Analysis
- How to structure a decision problem
- Decision tree
- Random forest

CHAPTER 4: Data analysis exercises

UCI machine learning repositories
Kaggle repository
Food database
Titanic dataset
Movie dataset
e-commerce dataset
Federal election commission dataset
CredirRisk dataset
Pima-indians dataset
World-alcohol dataset
Primary school dataset
CardioGoodfitness dataset
Car-mpg dataset

You will be going through detailed 2 to 3 months of Exploratory Data Analysis Python Training Hands-on training

Detailed instructor led sessions to help you become a proficient Expert in Exploratory Data Analysis Python Training.
Build a Exploratory Data Analysis Python Training professional portfolio by working on hands on assignments and projects.
Personalised mentorship from professionals working in leading companies.
Lifetime access to downloadable Exploratory Data Analysis Python Training course materials, interview questions and project resources.

Credo Systemz - Velachery, Chennai
Call Us +91 9884412301

Credo Systemz - OMR, Chennai
Call Us +91 9600112302

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Learning objectives of Exploratory Data Analysis Python Training

Hands on Knowledge on Python and Functional Programming
Working with third party modules
Real time experience in data with current scenario
Gaining the complete knowledge on Numpy and its arrays
Aspirants to be familiar with pandas and its features
Master in concept of Statistics and Hypothesis testing
Understand the concepts of Decision analysis using decision tree and random forest
Real time experience in school dataset, Movie dataset, Food database,e-commerce dataset, CardioGoodfitness dataset, etc.,

Data Analytics Course in Chennai

The need for Data Analytics certification course in Chennai has been increasing day by day because of the more number of available job opportunities for a professionally certified Data Analyst and Data Scientist.

Data Analytics course in Credo Systemz is been ranked as the No.1 training institute in Chennai because of the professional practical training approach handled down by experts in the field. Each course sections is been framed with assignments which relate the live scenarios and real-time processes.

Data Analytics Using Python Training In Chennai