Data Scientist

As part of this data science certification course, you will:

• Learn to apply effective prompt engineering techniques to improve the performance and control the behavior of generative AI models.
• Learn about Python for data science, prompt engineering, explainable AI, conversational AI, large language models, ChatGPT, and more.
• Understand generative AI, its landscape, and practical applications.
• Grasp fundamentals of Excel analytics functions and conditional formatting.
• Understand the tools and techniques used in business analysis planning and monitoring.
• Gain an in-depth understanding of data structure and data manipulation.
• Understand and use linear and non-linear regression models and classification techniques for data analysis.
• Obtain a comprehensive knowledge of supervised and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipelines.
• Gain practical mastery over data science principles, algorithms, and machine learning applications.
• Gain expertise in mathematical computing using the NumPy and scikit-learn packages.
• Use SciPy and its sub-packages, such as Integrate, Optimize, Statistics, IO, and Weave, to perform scientific and technical computing.
• Master the concepts of applied data science, recommendation engines, and time series modeling.
• Learn to visualize data using Tableau and Power BI and become proficient in building interactive dashboards.

As part of this data science certification course, you will:

• Learn to apply effective prompt engineering techniques to improve the performance and control the behavior of generative AI models.
• Learn about Python for data science, prompt engineering, explainable AI, conversational AI, large language models, ChatGPT, and more.
• Understand generative AI, its landscape, and practical applications.
• Grasp fundamentals of Excel analytics functions and conditional formatting.
• Understand the tools and techniques used in business analysis planning and monitoring.
• Gain an in-depth understanding of data structure and data manipulation.
• Understand and use linear and non-linear regression models and classification techniques for data analysis.
• Obtain a comprehensive knowledge of supervised and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipelines.
• Gain practical mastery over data science principles, algorithms, and machine learning applications.
• Gain expertise in mathematical computing using the NumPy and scikit-learn packages.
• Use SciPy and its sub-packages, such as Integrate, Optimize, Statistics, IO, and Weave, to perform scientific and technical computing.
• Master the concepts of applied data science, recommendation engines, and time series modeling.
• Learn to visualize data using Tableau and Power BI and become proficient in building interactive dashboards.

• Module One (T1) Introduction to Data Science
  Preview

  00:25:08

• Module Two (T2) Setup Anaconda Data Science
  Preview

  00:13:47

• Module Three (T3) - Running Python Basic Data Science [ Part-1]
  Preview

  00:49:54

• Module Three (T3) - Python Data Science [ Part-2]
  Preview

  00:43:51

• Module Four (T4) - Run EDA Files Data Science [ Part-1]
  Preview

  01:24:25

• Module Four (T4) - Run EDA Files Data Science [ Part-2]
  Preview

  01:16:16

• Module Four (T4) - Run EDA Files Data Science [ Part-3]
  Preview

  01:26:35

• T1 - Big Data
  Preview

  00:19:53

• T2 - Statistics For Data Science
  Preview

  00:28:06

• T3 - Excel Formulas & Functions
  Preview

  00:33:31

• T4 - Probability Definition in Math
  Preview

  00:14:20

• T5 - Inferential Statistics
  Preview

  00:58:00

• T6 - Tableau
  Preview

  01:18:13

• T7 - Machine Learning Algorithms
  Preview

  01:18:46

• Part 1 - Reading Time Series using Machine Learning Models
  Preview

  01:09:16

• Part 2 - Reading Time Series using Machine Learning Models
  Preview

  01:30:03

• Part 2 - Reading Time Series using Machine Learning Models
  Preview

  00:46:19

• Part 4 - Reading Time Series using Machine Learning Models
  Preview

  01:04:10

• Part 5 - Reading Time Series using Machine Learning Models
  Preview

  00:40:42

• Ensemble Learning in Machine Learning
  Preview

  01:11:34

• Part 1 - Decision and Classification Trees
  Preview

  01:03:23

• Part 2 - Decision and Classification Trees
  Preview

  00:16:53

• Part 1 - Logistic Regression in Deep Learning
  Preview

  01:03:23

• Part 2 - Logistic Regression in Deep Learning
  Preview

  01:03:23

• Reading Time Series Data and Stroing
  Preview

  00:58:42