Data Analytics & Visualization
Data Analytics & Visualization is the process of examining raw data to uncover patterns, trends, and insights, followed by presenting these findings in a clear and visually engaging format. This discipline combines analytical methods, statistical techniques, and visualization tools to transform complex data into actionable knowledge, aiding in better decision-making and strategic planning. It bridges the gap between data exploration and communication, making it easier for stakeholders to understand and act on data-driven insights.
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About Data Analytics & Visualization Course
Data Analytics & Visualization is the process of examining raw data to uncover patterns, trends, and insights, followed by presenting these findings in a clear and visually engaging format. This discipline combines analytical methods, statistical techniques, and visualization tools to transform complex data into actionable knowledge, aiding in better decision-making and strategic planning. It bridges the gap between data exploration and communication, making it easier for stakeholders to understand and act on data-driven insights.
Industry-Relevant Skills:
Master the tools and technologies that are in demand across leading industries.
Hands-On Experience:
Work on real-world projects to gain practical knowledge and confidence.
Expert Guidance:
Learn from experienced professionals who provide insights and mentorship.
Certification:
Industry renowned internship & course completion certification
Career Opportunities:
Enhance your employability with skills that stand out in the competitive job market.
Comprehensive Learning:
Gain a deep understanding of both foundational and advanced concepts.
Personal Growth:
Develop problem-solving, communication, and teamwork skills essential for success.
Career Transition:
More than 1200 learners upgraded their careers in the last 12 months
Course Offerings
Diverse Project Portfolio
Practice Exercises
Doubt Clearing Sessions
Dedicated Buddy Support
Industry Oriented Curriculum
Industry Recognized Certificate
Q&A Forum
Instructor Led Sessions
Peer to Peer Networking
Email Support
Mock Interviews
Module Level Assignments
Become a Tools Expert
Course Structure
Fundamentals
Key Areas Covered
Introduction to Data Science & Analytics
- Understanding the role of Data Science in modern business decision-making.
- Key differences between Data Science, Data Analytics, and Data Engineering.
- Real-world use cases across industries.
Data Types and Formats
- Structured, Semi-Structured, and Unstructured Data.
- Common data formats: CSV, JSON, Excel, SQL databases.
The Data Science Lifecycle
- Data Collection: Gathering data from various sources (databases, APIs, web scraping).
- Data Cleaning: Removing inconsistencies, handling missing data, and ensuring quality.
- Data Analysis: Identifying trends and patterns.
- Data Visualization: Presenting insights through graphs, charts, and dashboards.
- Interpretation and Communication of Results.
Tools for Data Science and Analytics
- Programming Languages: Python, R.
- Data Manipulation Libraries: Pandas, NumPy.
- Visualization Libraries: Matplotlib, Seaborn, Tableau.
- SQL for database querying and manipulation.
Introduction to Statistics
- Basic statistical measures (mean, median, mode, standard deviation).
- Probability basics and its application in data analysis.
Problem-Solving with Data
- Formulating data-driven questions.
- Exploratory Data Analysis (EDA) to uncover insights.
Ethics in Data Science
- Understanding bias in data.
- Privacy and confidentiality in handling datasets.
Basics
Introduction to Python Programming
Learn the basics of Python, including syntax, code structure, and running Python programs.Variables, Data Types, and Operators
Understand how to store, manipulate, and use data with variables, data types (strings, integers, etc.), and operators.Control Structures (if-else, loops)
Master decision-making and repetition in Python using conditional statements and loops.Functions and Modules
Explore reusable blocks of code with functions and how to use/import modules for added functionality.
Core
Introduction to Python Libraries: NumPy, Pandas, and Matplotlib
Get familiar with essential libraries for data manipulation, analysis, and visualization.Data Manipulation using Pandas
Learn to clean, filter, aggregate, and transform datasets efficiently using Pandas.Working with Arrays using NumPy
Perform numerical computations and work with multi-dimensional arrays using NumPy.File Handling and Exception Handling
Understand how to read/write files and handle errors gracefully during execution.
Intermediate
Exploratory Data Analysis (EDA) Techniques
Analyze datasets to discover patterns, trends, and insights using Python tools.Data Visualization with Seaborn and Matplotlib
Create stunning and informative charts and graphs to present your data.APIs and JSON Data Processing
Learn to interact with APIs to retrieve data and process JSON data for analysis.Basic Statistics using Python
Apply statistical methods like mean, median, mode, variance, and more using Python.
Advanced
Advanced Data Wrangling
Dive deep into reshaping, merging, and cleaning complex datasets for analysis.Database Integration with Python (SQLAlchemy, SQLite)
Connect Python to databases, perform queries, and integrate SQL for data storage and retrieval.Automation and Script Development
Build scripts to automate repetitive tasks and improve efficiency in data workflows.Real-World Applications and Case Studies
Apply all learned skills to real-world scenarios and work on hands-on projects to solidify knowledge.
Basics
Introduction to Statistics
Learn the foundation of statistics, including its role in data analysis and decision-making.Measures of Central Tendency (Mean, Median, Mode)
Understand how to summarize data using measures that represent the center of a dataset.Measures of Dispersion (Variance, Standard Deviation)
Explore how to measure data spread and variability within datasets.
Core
Probability Concepts and Distributions
Study the basics of probability, probability distributions (normal, binomial, etc.), and their applications.Hypothesis Testing
Learn to formulate and test hypotheses to draw conclusions about population parameters.Correlation and Regression Analysis
Understand relationships between variables and predict outcomes using linear regression models.
Intermediate
Sampling Methods and Techniques
Explore different sampling techniques and their importance in statistical analysis.Analysis of Variance (ANOVA)
Learn how to compare means across multiple groups to identify significant differences.Time Series Analysis
Study data that varies over time, identify trends, and make forecasts using statistical methods.
Advanced
Multivariate Statistics
Analyze datasets with multiple variables and understand techniques like PCA, cluster analysis, and factor analysis.Bayesian Statistics
Learn probabilistic inference using Bayes’ theorem and apply Bayesian approaches to statistical modeling.Statistical Model Validation
Ensure the accuracy and reliability of statistical models through validation techniques like cross-validation and residual analysis.
Basic Topics
Introduction to Data Visualization
Understand the importance of data visualization in data analysis and decision-making.Fundamentals of Visualizing Data
Learn key principles of effective data visualization, including clarity, simplicity, and accuracy.Creating Basic Plots (Bar, Line, Scatter)
Start creating simple yet powerful visualizations such as bar charts, line graphs, and scatter plots.
Core Concepts
Data Visualization with Matplotlib
Master Python’s foundational visualization library to create static and dynamic plots.Advanced Visualizations with Seaborn
Explore advanced statistical visualizations such as box plots, violin plots, and pair plots.Dashboard Creation with Tableau
Learn to build interactive dashboards and visual storytelling using Tableau.
Intermediate Topics
Interactive Visualizations using Plotly
Create interactive and engaging charts such as 3D plots, animated graphs, and more using Plotly.Storytelling with Data Visualizations
Learn to design visualizations that effectively communicate insights and narratives.Heatmaps, Pair Plots, and Other Advanced Charts
Dive into advanced visualizations like heatmaps and multi-variable pair plots for deeper insights.
Advanced Topics
Building Data Dashboards
Create comprehensive and interactive dashboards for real-time data monitoring and reporting.Best Practices for Effective Visualization
Learn design principles to ensure your visualizations are clear, impactful, and professional.Real-World Visualization Projects
Apply your skills to real-world projects, integrating multiple visualization tools and techniques.
Basic Topics
Introduction to Machine Learning
Learn the core concepts of Machine Learning, its applications, and its role in modern technology.Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
Understand the three main types of machine learning and when to apply each.Preparing Data for Machine Learning
Learn data preprocessing techniques, including cleaning, transforming, and splitting data for model training.
Core Concepts
Linear Regression and Logistic Regression
Explore regression techniques for predicting continuous and categorical outcomes.Clustering Techniques (K-Means, Hierarchical)
Learn how to group data points into clusters based on their similarities.Feature Engineering and Scaling
Master techniques to extract meaningful features and scale data for optimal model performance.
Intermediate Topics
Decision Trees and Random Forests
Understand tree-based algorithms for classification and regression tasks.Model Evaluation and Metrics (Confusion Matrix, ROC Curve)
Learn to evaluate model performance using metrics like accuracy, precision, recall, and ROC-AUC.Dimensionality Reduction (PCA)
Simplify datasets while retaining essential information using Principal Component Analysis (PCA).
Advanced Topics
Introduction to Neural Networks
Understand the basics of neural networks, including structure and activation functions.Basics of Deep Learning
Learn foundational concepts of deep learning, including multi-layered networks and backpropagation.Building End-to-End Machine Learning Models
Gain hands-on experience building and deploying machine learning models for real-world applications.
Basic Topics
Introduction to Big Data
Understand the concept of Big Data, its characteristics (volume, velocity, variety), and its importance in modern analytics.Basics of Distributed Systems
Learn how distributed systems enable Big Data processing by dividing tasks across multiple nodes.Overview of Hadoop Ecosystem
Get familiar with the key components of Hadoop, including HDFS, MapReduce, and YARN.
Core Concepts
Working with HDFS
Learn to store, manage, and retrieve large datasets using the Hadoop Distributed File System.Basics of Apache Spark
Explore the fundamentals of Apache Spark, a fast and general-purpose engine for large-scale data processing.Data Processing with MapReduce
Understand the MapReduce programming model for processing large datasets in a distributed environment.
Intermediate Topics
Real-Time Processing with Spark Streaming
Learn how to process and analyze streaming data in real-time using Apache Spark.Hive and Pig for Big Data Analysis
Explore Hive for SQL-like querying and Pig for data transformation and analysis in the Hadoop ecosystem.Introduction to Cloud Platforms for Big Data (AWS, Azure, GCP)
Discover how cloud platforms offer scalable solutions for Big Data storage and processing.
Advanced Topics
Handling Large Datasets with Spark MLlib
Use Spark’s MLlib library for scalable machine learning on large datasets.Optimizing Big Data Pipelines
Learn techniques to improve the efficiency and reliability of data pipelines.Big Data Project Implementation
Apply Big Data tools and techniques to build and deploy end-to-end projects in real-world scenarios.
Introduction to Node.js
- Introduction to Node.js:
- Setup:
node -v and npm -v.- First Steps:
Understanding Core Concepts
- Modules:
fs, http, path).- File System:
- Creating a Simple Server:
http module to create a basic web server.
Deep Dive into Asynchronous Programming
- Callbacks:
- Promises:
- Async/Await:
Build Projects from Scratch
Meet your Mentors
Our Alumni's are in
Get Certified
Once you complete the course offline, complete the assignment and project, you will be able to get the Course certificate.
After Live project work for a minimum of 1 month, you will receive an Internship Certificate.
- Attendance must be 60% offline Training Sessions and Internship.
- After scoring at least 60% in the quiz & assignments.,
- After Completing 1 Project in Internship Period.
Student Reviews
Success Stories
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Continuing MERN Training + Internship Program in TetraSkills
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Does TetraSkills provide job assistance and on-campus placement?
TetraSkills does not directly provide job assistance or on-campus placements. Instead, we focus on equipping students with the skills and experience needed to succeed in placements. Our offerings include:
- Mock interviews and group discussions.
- Practical skill-building exercises.
- Hands-on experience through internships with partner companies.
Through these programs, students become placement-ready and gain the confidence to excel in their career paths.