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Ekascloud

Data Analytics

  • 24/7 Support
  • 4 Months
  • 120 Sessions

Course Description

Explore the dynamic realm of data analytics in our comprehensive course designed to empower you with the skills needed to navigate and derive valuable insights from vast datasets. Dive into the fundamentals of data collection, cleansing, and visualization using cutting-edge tools and techniques. Gain proficiency in statistical analysis, predictive modeling, and machine learning algorithms to uncover patterns and make informed decisions. Develop hands-on expertise with industry-standard software and platforms, equipping you to extract meaningful conclusions for business intelligence. Whether you're a beginner or seasoned professional, this course cultivates a solid foundation in data analytics, fostering your ability to transform raw information into actionable intelligence.

One to One personalized training Schedule for Data Analytics

EkasCloud provides flexible training to all it's student. Here is our training schedule. Incase you find these timings difficult, please let us know. We will try to arrange appropriate timings based on your Convenience.

24-10-2024 Thursday (Monday - Friday) Weekdays Regular 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session
26-10-2024 Saturday (Monday - Friday) Weekdays Regular 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session
28-10-2024 Monday (Monday - Friday) Weekdays Regular 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session
29-10-2024 Tuesday (Monday - Friday) Weekdays Regular 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session

Course Detail

Explore the dynamic realm of data analytics in our comprehensive course designed to empower you with the skills needed to navigate and derive valuable insights from vast datasets. Dive into the fundamentals of data collection, cleansing, and visualization using cutting-edge tools and techniques. Gain proficiency in statistical analysis, predictive modeling, and machine learning algorithms to uncover patterns and make informed decisions. Develop hands-on expertise with industry-standard software and platforms, equipping you to extract meaningful conclusions for business intelligence. Whether you're a beginner or seasoned professional, this course cultivates a solid foundation in data analytics, fostering your ability to transform raw information into actionable intelligence.

  1. In-demand Skills: Data analytics is a high-demand skill in today's job market, opening up diverse career opportunities in various industries.

  2. Informed Decision-Making: Learn to make informed decisions based on data-driven insights, contributing to better business strategies and outcomes.

  3. Business Intelligence: Acquire the ability to extract valuable information from data, providing a competitive edge by understanding market trends and customer behavior.

  4. Problem Solving: Develop problem-solving skills by analyzing complex datasets, identifying patterns, and proposing effective solutions to real-world challenges.

  5. Career Advancement: Enhance your professional profile and increase your marketability by adding data analytics expertise to your skill set.

  6. Innovation: Drive innovation within organizations by leveraging data to discover new opportunities, streamline processes, and optimize performance.

  7. Adaptability: Equip yourself with skills that are transferable across industries, enabling you to adapt to the evolving landscape of technology and business.

  8. High Earning Potential: Positions in data analytics often come with competitive salaries due to the increasing demand for skilled professionals in the field.

  9. Global Relevance: Data analytics is a globally relevant skill, allowing you to work and contribute to organizations worldwide.

  10. Continuous Learning: Join a field that is constantly evolving, providing ongoing learning opportunities and the chance to stay at the forefront of technological advancements.

• We check your knowledge before we start the session.

• We build foundational topics first and core topics next.

• Theory classes with a real-time case study.

• Demo on every topic.

• You will learn how to design architecture diagrams for each service.

• Mock exam on every topic you understand.

• Exam Preparation

• Interview Preparation

Data Analytics Syllabus

View Syllabus

4 Months Course 50% Theory 50% Lab Daily Homework Real time Projects

Topics Covered

Module 1: Foundations of Data Analytics

1.1 Introduction to Data Analytics

  • Definition, scope, and significance of data analytics
  • Historical perspective and evolution of the field
  • Key components of the data analytics ecosystem

1.2 Data Collection and Preprocessing

  • Techniques for data acquisition (APIs, web scraping, databases)
  • Data cleaning and preprocessing using Pandas
  • Exploratory Data Analysis (EDA) and feature engineering

1.3 Descriptive Statistics and Visualization

  • Measures of central tendency and dispersion
  • Data visualization using Matplotlib, Seaborn, and Plotly
  • Dashboard creation and design principles

1.4 Inferential Statistics

  • Hypothesis testing, confidence intervals, and p-values
  • Regression analysis and correlation
  • Advanced statistical methods and experimental design

Module 2: Programming for Data Analytics

2.1 Python Programming Fundamentals

  • Variables, data types, and control structures
  • Functions, modules, and error handling
  • Object-oriented programming concepts

2.2 Data Manipulation and Visualization with Python

  • Pandas for data manipulation
  • Data visualization libraries (Matplotlib, Seaborn, Plotly)
  • Interactive visualizations and geospatial data

2.3 Advanced Topics in Python

  • Web scraping using BeautifulSoup and Selenium
  • Machine learning libraries (Scikit-Learn, TensorFlow, PyTorch)
  • API integration and working with JSON data

2.4 Database Management and SQL

  • Relational databases and normalization
  • SQL basics and advanced querying
  • Connecting Python to databases for seamless integration

Module 3: Machine Learning and Predictive Analytics

3.1 Introduction to Machine Learning

  • Supervised learning, unsupervised learning, and reinforcement learning
  • Model evaluation, validation, and overfitting
  • Ensemble methods and model deployment considerations

3.2 Regression and Classification Models

  • Linear regression, logistic regression
  • Decision trees, random forests, and support vector machines
  • Neural networks and deep learning fundamentals

3.3 Clustering and Dimensionality Reduction

  • K-means clustering, hierarchical clustering
  • Principal Component Analysis (PCA) and t-SNE
  • Anomaly detection and outlier analysis

3.4 Time Series Analysis and Forecasting

  • Time series data exploration and preprocessing
  • ARIMA models and forecasting techniques
  • Advanced time series models and applications

Module 4: Big Data and Advanced Technologies

4.1 Introduction to Big Data

  • Characteristics of big data and its challenges
  • Distributed computing frameworks (Hadoop, Spark)
  • Big data storage solutions (HDFS, S3, Google Cloud Storage)

4.2 Cloud Computing for Data Analytics

  • Cloud platforms (AWS, Google Cloud, Azure)
  • Setting up cloud-based data analytics environments
  • Scalability, security, and cost considerations

4.3 Advanced Analytics with Spark

  • Spark architecture and RDDs
  • Spark SQL and DataFrame API
  • Machine learning with Spark MLlib

Module 5: Capstone Project and Industry Applications

5.1 Capstone Project

  • Real-world data analytics project in collaboration with industry partners
  • Project planning, execution, and presentation
  • Peer review and feedback sessions

Instructor

Vijilin Jerrish

jerrish has a Bachelors degree in Computer Science, He is based out of India and had trained more than 8000 students in last 9years on Networking, Linux, Automation and Cloud Technologies.

Jerrish is RHCSA, Ansible, AWS Solutions Architect Pro, Terraform and Kubernetes Certified

Frequently asked question

Q: What if I miss a class?
A: We will stop the course for you because it is one-to-one or one-to-two students only.

Q: What if I am not an Engineer/Programmer? Can I still do the Data Analytics course?
A: Data Analytics is not a separate domain but a tool/technology which can be used in any field. Our course is designed to address the needs of non-programmers and candidates who have no IT knowledge. Anyone who has an interest in Data Analytics can take up this course.

Q: Will I get placement assistance?
A: Yes. You will get it once you finish the course.

Q: What if I have queries after I complete this course?
A: You can check our blog or send your queries from social media like Facebook, Linkedin, Instagram, Twitter, and Youtube.

Q: How soon after signing up will I get access to the course?
A: Once you join the course, our Counselor Manager book a slot with our Trainer based on your and Trainer's available time.

Q: Is the course material accessible to the students even after the course training is over?
A: Yes, you can access our course material. Not only material, but you can also watch short and sweet videos from our Youtube Channel.

Q: What is the average salary of a Data Analytics professional?
A: $229,868

Admission Process

If a student want to take admission in any course he has to go with the following steps

Step 1
1 Hour Interview
  • Discuss Learning Goals: Understand the candidate’s career objectives, learning expectations, and prior experience (if any).
  • Personalized Course Recommendation: Based on the discussion, recommend the most suitable course.
  • Course Customization: Tailor the course plan to fit the candidate’s needs, including scheduling flexibility.
Step 2
3 Hour Assessment Session
  • Technical Skills Evaluation: Hands-on tasks or exercises to evaluate the candidate’s current technical understanding (for advanced courses).
  • Cloud Fundamentals Check: For entry-level courses, a basic assessment of cloud knowledge and IT skills.
  • Feedback & Results: Provide instant feedback and suggest an appropriate course path based on assessment performance.
Step 3
Final Enrollment

Upon successful completion of the assessment, candidates receive a customized learning path, course schedule, and payment options. Candidates can finalize their enrollment by agreeing to the course structure and payment plan.

Data Analytics Fees
£ 4000
Enroll Now