Big Data Analytics
(217 Ratings)
☆☆☆☆☆ 4.9/5
Big Data Analytics is the process of looking at big, complicated datasets and figuring out what they can tell us. It is a set of methods and tools for collecting, storing, processing, and analyzing data that go beyond what standard data processing systems can do. By using tools like Hadoop, Spark, and NoSQL databases, companies can find useful patterns, trends, and connections in huge amounts of data. Big Data Analytics makes it possible to make decisions based on data, create predictive models, and find business possibilities. It’s especially helpful in fields like marketing, finance, healthcare, and scientific research, where a lot of data can give you an edge over your competitors and help you come up with new ideas.
- 15+ Courses
- 25+ Projects
- 100 Hours
Overview Big Data:
Welcome to our Big Data Analytics Training program, which covers a lot of ground. In the data-driven world of today, businesses need to use the power of big data to make smart choices, gain valuable insights, and drive innovation. Our training is meant to give you the skills and knowledge you need to be a good big data scientist and get useful information from large datasets.
Why Big Data Analytics?
With big data analytics, companies can find patterns, trends, and connections in very large and complicated information. Here’s why it’s important for a business to use analytics big data:
Informed Decision-Making:
Organizations can make strategic and well-thought-out choices with the help of data-driven insights.
Business Innovation:
Big data analytics drives innovation by finding new ways to grow and new possibilities.
Competitive Edge:
Organizations that use big data to understand customer behavior and market trends gain a competitive edge.
Efficiency:
It is better because insights from big data help optimize processes and the use of resources.
Key Highlights of Our Training:
Introduction to Big Data Analytics:
Learn about the concepts, technologies, and benefits of big data analytics.
Data Acquisition and Preprocessing:
Learn about how to get data, clean it up, and change it so it’s ready for study.
Data Exploration and Visualization:
Investigate ways to explore data, show it visually, and find patterns.
Statistical Analysis:
Learn statistical methods for checking hypotheses, analyzing correlations, and making models that can predict what will happen.
Machine Learning:
Learn how to use machine learning algorithms to look at big data and guess what will happen.
Big Data Tools:
Look into popular big data tools like Hadoop, Spark, and NoSQL systems for working with and processing large datasets.
Data Mining:
Learn how to mine data to find insights and trends that are hidden in it.
Real-Time Analytics:
Learn about real-time data and stream processing so you can make decisions quickly.
Why Choose Our Big Data Analytics Training?
Expert Teachers:
Learn from expert data analysts who share real-world tips and the best ways to do things in the field.
Hands-On Learning:
Through data analysis projects, simulations, and tasks, you can get real-world experience.
Industry Relevance:
Get skills that are in high demand in the industry for getting information from big data.
Career Advancement:
If you know how to analyse big data, you can move up in your career in data science, business intelligence, and analytics.
Flexible Learning Formats:
You can choose to take online courses at your own pace or attend live sessions led by a teacher to fit your schedule.
Who Should Attend:
- Data analysts and people who work in business intelligence
- IT professionals who are interested in analyzing big data
- People who work with data and science
- Anyone interested in finding answers in big data
Why Choose Checkmate IT Tech?
If you are looking for training providers that offer ongoing support and resources to help you succeed in your Big Data Analytics journey. Checkmate IT offers a comprehensive introduction to Big Data Analytics, including both theoretical and practical aspects. These may include access to trainers, online communities, practice exercises, and job placement assistance. Checkmate IT Tech offers flexible training options that suit your schedule and learning preferences.
Curriculum
Module 1: Introduction to Big Data Analytics
- Understanding Big Data Concepts and Importance
- Role of Big Data Analytics in Business Decision-Making
- Overview of Big Data Analytics Tools and Technologies
Module 2: Data Collection and Storage
- Data Collection Methods: Structured and Unstructured Data
- Introduction to Data Lakes and Data Warehouses
- Choosing the Right Storage Solutions for Big Data
Module 3: Data Preprocessing and Cleaning
- Exploratory Data Analysis (EDA) in Big Data
- Data Cleaning and Transformation Techniques
- Handling Missing Values and Outliers
Module 4: Data Integration and ETL
- Extract, Transform, Load (ETL) Process in Big Data
- Data Integration Techniques: Batch and Real-Time
- Implementing ETL Workflows with Big Data Tools
Module 5: Big Data Processing Frameworks
- Introduction to Hadoop Ecosystem: HDFS, MapReduce, YARN
- Overview of Apache Spark for Big Data Processing
- Comparing Batch and Stream Processing
Module 6: Data Analysis with SQL and NoSQL
- Querying and Analyzing Big Data with SQL
- Using NoSQL Databases for Unstructured Data Analysis
- Combining Structured and Unstructured Data Analysis
Module 7: Data Visualization and Interpretation
- Importance of Data Visualization in Big Data Analytics
- Creating Interactive Dashboards and Reports
- Using Visualization Tools (e.g., Tableau, Power BI)
Module 8: Machine Learning for Big Data
- Machine Learning in analytics big data
- Big Data Feature Engineering and Model Building
- Scalable Machine Learning with Distributed Computing
Module 9: Text and Sentiment Analysis
- Processing and Analyzing Textual Data at Scale
- Sentiment Analysis and Text Classification
- Leveraging NLP Techniques for Insights
Module 10: Predictive Analytics and Time Series Analysis
- Building Predictive Models with Big Data
- Time Series Analysis and Forecasting
- Handling Large-Scale Time Series Data
Module 11: Final Projects and Course Wrap-up
- Guided Hands-On Projects: Performing Big Data Analytics on Real Datasets
- Student Presentations and Analysis Demonstrations
- Review of Key Concepts and Takeaways
- Discussion of Further Learning Paths and Resources
- Certificates of Completion
Career Transition
“The best part of training was working on a team project in which we analyzed a huge set of data and showed our findings to experts in the field. I got a coveted job as a data analyst at a well-known tech company “Splunk” because of this training. I can say for sure that this course took my work to a new level.”
Oman Lee
“I’m a data Analyst. This course developed my interest in statistical analysis on a higher level. It gave me the skills I needed to make a difference in an area that is changing quickly”.
Bruce Martan
“I chose the Big Data Analytics training for more knowledge in my field. From the first day, the interesting talks and hands-on workshops kept me interested. I’m proud to say that I just had a study paper published that used some of the methods I learned in the course”.
Khatib Khawaja
Previous
Next
Skills:
- The Hadoop Ecosystem (HDFS, MapReduce, Hive, Pig, etc.)
- NoSQL databases, such as MongoDB and Cassandra
- Concepts of Data Warehousing
- Principles of Distributed Computing
- ETL (Extract, Transform, Load) and data ingestion
- Optimizing SQL and Query for Big Data
- Tools for seeing data (like Tableau and Power BI)
- Statistical analysis and machine learning
- Spark Streaming and Apache Spark
- Data mining and analytics for making predictions
- Analytics in real time
- Privacy and safety of data in big data
- Big Data platforms in the cloud, such as AWS, Azure, and Google Cloud
- Architecture of the Data Lake and Managing It
- The Governance and Compliance of Big Data
Meet Your Mentors
Dr. Emily Chen, PhD
Dr. Emily Chen has a doctorate in data science. Chen is an expert in big data apps that use data preprocessing, machine learning, and predictive modelling.
Training Insights: Dr. Chen will teach the basics of big data analytics, data extraction, transformation, and loading (ETL), as well as distributed computing frameworks like Hadoop and Spark in her training classes. She will lead students through hands-on projects that help them study and show how big data sets are put together.
Michael Johnson
Michael Johnson is a Big Data Engineer who has worked with data engineering and cloud computing. He has worked with tools like Apache Hadoop, Apache Spark, and cloud-based data systems like Google Big Query and Amazon Redshift. Michael is an expert at building data pipelines and finding the best way to store data.
Course Insights: Michael’s class will be about handling big data using distributed frameworks, data warehousing, and real-time analytics. He will show how to make scalable data processes, change data, and make interesting visualizations from big data sets.
Alexandra Lee
Alexandra Lee is an experienced Data Analyst. She has used big data tools to work on projects involving data exploration, pattern recognition, and trend analysis. Alexandra is proficient at analyzing data with tools like Python, R, and Tableau.
Insights into the Course: Alexandra’s training sessions will focus on how to analyze and display big datasets. She will teach students how to use experimental data analysis, statistical techniques, and the best ways to show stakeholders what they can learn from big data.
Program Fee
Fee
2950$
Admissions are closed once the requisite number of participants enroll for the upcoming cohort. Apply early to secure your seat.
“Begin your journey with a 20% upfront payment, and our dedicated associate will guide you through the enrollment process.”
Career Services By Checkmate IT Tech
Job Assistance Interview Preparation Profile Building
Job Assistance
Placement Assistance
Placement opportunities are provided once the learner is moved to the placement pool. Get noticed by our 400+ hiring partners.
Exclusive access to Checkmate IT Tech Job portal
Placement opportunities are provided once the learner is moved to the placement pool. Get noticed by our 400+ hiring partners.
Interview Preparation
Mock Interview Preparation
Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.
One-on-one Career Mentoring Sessions
Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learner’s educational background, past experience, and future career aspirations.
Profile Building
Career Oriented Sessions
Over 10+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions that will help you stay on track with your upskilling.
Resume & LinkedIn Profile Building
Get assistance in creating a world-class resume & Linkedin Profile from our career services team and learn how to grab the attention of the hiring manager at the profile shortlisting stage
Placement Assistance
Placement opportunities are provided once the learner is moved to the placement pool. Get noticed by our 400+ hiring partners.
Exclusive access to Checkmate IT Tech Job portal
Placement opportunities are provided once the learner is moved to the placement pool. Get noticed by our 400+ hiring partners.
Mock Interview Preparation
Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.
One-on-one Career Mentoring Sessions
Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learner’s educational background, past experience, and future career aspirations.
Career Oriented Sessions
Over 10+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions that will help you stay on track with your upskilling.
Resume & LinkedIn Profile Building
Get assistance in creating a world-class resume & LinkedIn Profile from our career services team and learn how to grab the attention of the hiring manager at the profile shortlisting stage
Frequently Asked Questions
What is Big Data Analytics definition, and why is it so important?
In order to find hidden patterns, correlations, and insights that may be utilized to improve judgments and forecasts, big data analytics entails analyzing vast and diverse information. It is essential because it helps businesses to better understand the vast amounts of data they create and gather, which may lead to better customer experiences, increased operational efficiency, well-informed strategic planning, and a competitive edge in today’s data-driven marketplace.
Do I need a strong background in data science before I can learn Big Data Analytics?
While understanding Big Data Analytics doesn’t necessarily need having a strong experience in data science, it can be a good starting point. It is sufficient to have a basic understanding of data manipulation ideas, Python or R computer languages, and statistics. Individuals can gradually become proficient with Big Data Analytics methodologies and tools with commitment and focused learning. Hands-on projects and real-world experience will be essential for enhancing learning and field expertise.
What skills will I learn if I study Big Data Analytics?
Learning Big Data Analytics gives you the ability to process and analyze data, as well as manipulate data using computer languages like Python, R, and SQL. You will gain knowledge of how to process massive amounts of data and do machine learning using frameworks and technologies like TensorFlow, Spark, and Hadoop. In addition, you’ll get a thorough grasp of statistical tools for deriving significant patterns and trends from huge datasets, as well as proficiency in data visualization approaches for successfully communicating insights.
What kinds of things are taught in a normal course on Big Data Analytics?
Data preprocessing, data mining methods, machine learning algorithms for predictive analytics, data visualization, distributed computing frameworks like Hadoop and Spark, and database technologies like NoSQL are just a few of the subjects covered in a typical course on big data analytics certification. In order to extract useful insights for decision-making, students also learn about ethical considerations, practical applications, and best practices for handling large-scale datasets.
Is Big Data Analytics certified in any way?
There are qualifications for Big Data Analytics, so the answer is yes. Cloudera Certified Data Analyst, Microsoft Certified: Azure Data Scientist, and Google Cloud Professional Data Engineer are all examples of such things.
How long does it take to learn about Big Data Analytics?
It will take 2 months.
Is there guidance or help finding a job after the training?
Yes, we do offer help after training to help you get a job. This help includes access to tools, chances to meet new people, help with making a resume, and help getting ready for an interview.
What projects can I do to practice my skills with Big Data Analytics?
You can work on projects like analyzing customer behavior data for marketing insights, predicting sales trends using past data, doing sentiment analysis on social media data, or building recommendation systems.
Can I use Big Data Analytics to focus on a certain industry?
Yes, you can focus on a certain field, such as banking, healthcare, e-commerce, manufacturing, or any other field where data analysis can give you useful information.
What kinds of jobs can you get if you learn Big Data Analytics?
A career in big data analytics can lead to a number of opportunities, including those in machine learning, data engineering, business intelligence, data science, and data analysis. These experts use data to get insightful information, make wise decisions, and propel corporate success through data-driven strategies and solutions. They operate in a variety of industries, including technology, healthcare, finance, and e-commerce.