A short-term course in data analysis

Data Analytics Premium Course

ADMEC Multimedia > Certificate Courses > Data Analytics Premium Course
Eligibility
Graduation Not Required
Duration
04 Months
Training Type
Classroom, Online

Most Advanced Data Analytics Course Covering Statistics, Probability, Python, NumPy, Pandas, Matplotlib, and SciPy.

This course is designed to take your analytical and programming skills to the next levels. We will teach you all the fundamental concepts needed to analyze data to take useful insights from that.

Data Analytics is one of the most important sub-set of Data Science and data analysis using Python is trending thus Python is becoming a popular and powerful language among data analysts.

Why we’re the best in Data Analytics Training?

  • Across-the-board Curriculum
  • Selected Mentorship
  • Persistent Career Care

Tools and Technologies

 

Tools Covered
  • Statistics
  • Excel
  • SQL
  • Python
  • NumPy
  • Pandas
  • Matplotlib
  • SciPy
  • Flask Framework
Key Takeaways
  • Exploring Statistics for Data Analysis
  • Traditional Data Analysis on Excel
  • Diving Deeper into SQL
  • Analysis of Data Using Python
  • Numerical Computing with NumPy
  • Data Manipulation with Pandas
  • Data Visualization with Matplotlib
  • Data Manipulation and Visualization with SciPy
  • Developing Interactive UI Using Flask
  • Data Analytics Project

A professional is incomplete without a strong portfolio. With lifetime placement assistance, we will take care of your resume & portfolio development too.

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We are sure you are not fully convinced with above content and having many questions. Below we have given few important topics to go through to know more about the eligibility criteria, course structure, projects completed, career options etc.

Please check out our other Data Analytics courses too:

Entry requirements

Our Python Data Analytics Premium course is for anyone who wants to learn data analytics and visualization to draw the useful insights out of that which further help businesses to grow. You must have analytical and logical mindset to accomplish in this field.

No programming experience required.

Who can join this course

This course is best for anyone who loves to work with huge data to get some hidden information which provide solutions to businesses to grow in their domains. You are welcome if you have analytical thinking with interest in programming to find the insights.

Career opportunities

After successful completion of our Python Data Analytics Master Program, you are ready to apply as SQL Expert, Senior Data Analyst, Python Data Analyst, Business Analyst, and Python Developer.

Final projects and tests

Our experienced faculty will work on a ‘Data Analytics Project from Scratch’ with you to enable you to work in data analytics industry as an expert after completion of this advanced course.

Course structure and modules

Module 1 – Data Analysis Basics

  • Introduction to Data Analytics
  • Data Analytics in Real Life
  • Data Analysis vs Data Analytics
  • Common Tools and Technologies
  • Data and Its Importance
  • Common Steps for Data Analysis
  • Math Refresher

Module 2 – Statistics in Data Analysis

  • Statistics in Data Analysis
  • Types of Data
  • Data Table, Charts, and Graphs
  • The Fundamentals of Descriptive Statistics
  • Measures of Central Tendency, Asymmetry, and Variability
  • Dispersion and Distributions
  • Probability
  • Probability Distribution
  • Sampling and Sampling Distribution
  • Distribution of Sample Means, Population, and Variance
  • Confidence Interval Estimation: Single Population
  • Hypothesis Testing
  • Error Handling in Hypothesis Testing
  • Sample Size Calculation and One Way ANOVA
  • Post Hoc Analysis (Tukey’s test)
  • Randomize Block Design (RBD)
  • Two Way ANOVA
  • Linear Regression
  • Estimation and Prediction of Regression Model Residual Analysis
  • Multiple Regression Model
  • Categorical Variable Regression
  • Maximum Likelihood Estimation
  • Logistic Regression
  • Linear vs Logistic Regression Model
  • Confusion Matrix and ROC
  • Performance Testing of Logistic Model
  • Building Regression Analysis Model
  • Chi-square Test of Independence
  • Chi-square Goodness of Fit Test
  • Data Mining Using Cluster Analysis
  • K- Means Clustering
  • Hierarchical Method of Clustering
  • Classification and Regression Trees
  • Measures of Attribute Selection

Module 3 – Data Analysis Practical

  • Basics of Python
  • Python Data Structure
  • Flow Control Statements
  • Object-Oriented Programming
  • Setup of Anaconda
  • Jupyter Notebook
  • Numerical Computing with NumPy
  • Data Manipulation with Pandas
  • Data Visualization with Matplotlib
  • Data Manipulation and Visualization with SciPy

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Facilitating the concept of overall development, we organise various events and activities for 360 degree development of our students.

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