Data Analysis - Full Course

Estimated Duration

90 Days


Nagul Meera Shaik


INR 19,500.00

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Started on: 05 Jun 2024
Time: IST 07:30 am TO 08:30 am
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Course Description

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. It involves various techniques and methodologies to extract meaningful insights from raw data. Here's a detailed description of the components involved in data analysis:

  1. Data Collection: The first step in data analysis is gathering relevant data from various sources, which can include databases, spreadsheets, surveys, sensors, social media, etc. Ensuring the quality and integrity of the data is crucial at this stage.

  2. Data Cleaning and Preprocessing: Raw data often contains errors, missing values, inconsistencies, and outliers. Data cleaning involves techniques to identify and rectify these issues, ensuring the accuracy and consistency of the data. Preprocessing may also involve tasks like normalization, transformation, and feature engineering to prepare the data for analysis.

  3. Exploratory Data Analysis (EDA): EDA involves visually exploring and summarizing the main characteristics of the data using statistical graphics and descriptive statistics. This step helps in understanding the structure, patterns, and relationships within the data, uncovering initial insights, and guiding further analysis.

  4. Data Modeling and Analysis: In this phase, various statistical, machine learning, or other analytical techniques are applied to the cleaned and preprocessed data to extract meaningful patterns, trends, and relationships. This could include regression analysis, classification, clustering, time series analysis, etc., depending on the nature of the data and the objectives of the analysis.

  5. Interpretation and Inference: Once the analysis is performed, the results need to be interpreted in the context of the problem domain. This involves drawing conclusions, making predictions, and deriving actionable insights from the analyzed data. It's essential to communicate findings effectively to stakeholders, often using visualizations, reports, or presentations.

  6. Validation and Iteration: Data analysis is an iterative process. It's crucial to validate the results obtained through various means, such as cross-validation, hypothesis testing, or comparing with external sources. If necessary, the analysis process may need to be refined or repeated with additional data or different techniques to improve accuracy and reliability.

  7. Decision Making and Action: Finally, based on the insights derived from the data analysis, informed decisions can be made to address the problem or achieve the objectives at hand. These decisions could range from strategic planning, process optimization, product development, marketing strategies, risk management, etc.

Data analysis is a versatile process applicable across various domains, including business, science, healthcare, finance, marketing, and many others, helping organizations gain a competitive advantage and drive evidence-based decision-making.

Course Syllabus

Course Syllabus

  Python programming
1 Intoduction to Python programming Environment setup
2 Data Types and Variables  
3 Operators and Expressions  
4 Control Flow  Conditional Statements
5 Functions  
6 Comments and Indentation  
7 Debugging and Error Handling  
8 Data Structures  
9 Object-Oriented Programming (OOP)  
10 Exception handling  
11 Modules and Packages  
1 The Foundation for Numerical Computing with Python Arrays
Data Types
2 Creating and Working with Arrays Array Creation
Indexing and Slicing
Array Operations
3 Linear Algebra Functions Matrices
Linear Algebra Operations
  Wrangling Your Data in Python
1 Core Data Structures Series
2 Importing and Working with Data Data Sources
Data Cleaning and Manipulation
Indexing and Selection
3 Data Analysis with Pandas Time Series Analysis
Merging and Joining Data
Reshaping and Pivoting Data
4 Beyond the Basics GroupBy Operations
Data IO and Export
  Skills acquired at the end 1. Read and understand a Python code,
2. Handle and manage data tables
3. Interrogate, manipulate, order and modify
a dataset with Python
1 Matplotlib: The King of Visualization in Python Figure and Axes
Plot Types
2 Creating Basic Plots Importing Libraries
Data Preparation
Creating Plots
3 Advanced Plotting Features Subplots
4 Integration with Other Libraries- Pandas  
  Saving and Exporting Plots  
  Algorithms and methodology for
classification with Scikit-Learn
Presentation of classification algorithms
(Logistic regression, kNN, Decision tree,
Random forest, SVM...)
 Boosting and Bagging algorithms
 Model selection
 Classification of unbalanced data
  Regression methods Simple and multiple linear regression
 Regularized linear regression (Lasso,
Ridge and Elastic Net
  Data Analysis
  Data Analysis Principal Component Analysis
 Linear Discriminant Analysis (LDA)
 Clustering with the K-means algorithm
  Text Mining Introduction to regular expressions
 Managing textual data
 Creation of wordclouds
 Sentiment analysis
  Webscraping Introduction to web language (HTML, CSS)
 Web content extraction with
 Application of scrapping on Google
  Tableau Connection to data sources
 Data Formatting
 Data Visualization
Java Python Java Full Stack Spring Boot C++ Core Java MySQL Oracle Database Administrator Spring Mvc C Programming Spring Rest Api Java Script (ES6)

Nagul Meera Shaik

Professional Instructor
Experience: 9 years

About Nagul Meera Shaik

Overall 9 years of experience as a Technical Trainer and Developer in full stack java technologies like core java, hibernate, spring machines, spring boot and database technologies.


C Programming, C++, Core Java, Java, Java Full Stack, Java Script (ES6), MySQL, Oracle Database Administrator, Python, Spring Boot, Spring Mvc, Spring Rest Api

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