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Master Certification in Data Analytics & Data Science

  • Last Update August 12, 2023
  • 0 already enrolled


Data Science & Analytics is a multidisciplinary field that involves extracting valuable insights and knowledge from large volumes of data to support informed decision-making. It encompasses various techniques, processes, algorithms, and tools to analyze, interpret, and visualize data in order to uncover patterns, trends, correlations, and other meaningful information.

Accredited By:

An ISO 9001:2015 &
Teacher Scientist Network Certified

Course Overview:


Welcome to the Professional Certification Program in Data Science and Data Analytics! This comprehensive 90-day course is designed to introduce beginners to the exciting world of data science and data analytics. With over 20 years of industry and teaching experience, our expert instructors will guide you through the fundamentals of data analysis, data manipulation, and basic machine learning techniques. By the end of this program, you will have a solid foundation in data science that will empower you to explore various career opportunities in this rapidly growing field.

Course Objectives:


– Learn the fundamental concepts of data science and data analytics.

– Develop essential data manipulation and analysis skills using Python and Excel.

– Understand the basics of statistical analysis and hypothesis testing.

– Gain insights into the principles of machine learning and its applications.

– Work on hands-on projects to apply your knowledge in real-world scenarios.


Course Structure:


The 90-day program is divided into ten modules, each focusing on core concepts in data science and data analytics:

– What is Data Science?

– Role of Data Scientist

– Overview of the Data Science Workflow

– Data Sources and Types

– Data Collection Methods

– Data Cleaning and Preprocessing

– Exploratory Data Analysis (EDA)

– Data Visualization Techniques

– Matplotlib or Seaborn for Data Visualization

– Introduction to Pandas library

– Data Wrangling with Pandas

– Basic Data Analysis using Pandas

– Descriptive Statistics

– Inferential Statistics

– Hypothesis Testing

– Introduction to Machine Learning

– Supervised and Unsupervised Learning

– Overview of Algorithms (Linear Regression, Logistic Regression, Decision Trees, etc.)

– Training and Testing Data

– Model Evaluation Metrics

– Cross-Validation

– Introduction to Tableau or Power BI

– Creating Interactive Visualizations

– Basic SQL Queries

– Data Manipulation with SQL

– Python Basics

– NumPy and SciPy for Data Science

– Introduction to Jupyter Notebooks


– Analyzing and visualizing the distribution of data.

– Identifying trends and relationships between variables.

– Drawing insights and making recommendations based on the data.

– Building a machine learning model to predict a target variable.

– Evaluating the model’s performance using various metrics.

– Tuning hyperparameters to improve the model’s accuracy.

– Creating an interactive data visualization dashboard using tools like Tableau or Power BI.

– Presenting key insights and trends from a dataset in an easy-to-understand manner.

– Identifying and handling missing values in a dataset.

– Cleaning and transforming data for analysis and modeling.

– Analyzing time series data to identify patterns and trends.

– Forecasting future values using time series analysis techniques.

– Performing data queries and analysis using SQL on a given database.

– Extracting insights and information from the database.

– Analyzing text data to determine sentiment polarity (positive, negative, neutral) using natural language processing techniques.

– Creating a compelling data-driven narrative to communicate insights effectively

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  • Course level: Beginner
  • Total Enrolled 0
  • Last Update August 12, 2023
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