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What you will learn

After completing this course, a trainee will be able to:

  • Complete Python Programming, Python Numpy, Python Matplotlib, Scikit-Learn, Data Analysis
  • Machine Learning With Python, Python Scipy, Practise Papers and Quizes, Real Life Projects, K-nearest neighbors (KNN)
  • Mean Median Mode, Standard Deviation, Data Distribution, Normal Data Distribution, Scatter Plot
  • Linear Regression, Polynomial Regression, Multiple Regression, Scale, Train/Test, Confusion Matrix
  • Hierarchical Clustering, Logistic Regression, Grid Search, Categorical Data, K-means, Cross Validation
  • Python Data Types, Python Lists, Python Tuples, Python Sets, Python Dictionaries, Python If ... Else
  • Python While and for Loops, Python Arrays, Python Classes and Objects, Python Modules, Python Iterators, Python Try Except
  • NumPy Data Types, NumPy Array Iterating, NumPy Random, Random Data Distribution, Pandas DataFrames, Pandas - Analyzing DataFrames

Data Science is an interdisciplinary field that leverages statistical analysis, data exploration, and machine learning techniques to derive knowledge and meaningful insights from data.

Data Science encompasses various processes, including data acquisition, thorough analysis, and informed decision-making. Data Science involves the identification and interpretation of data patterns to make predictive assessments.

Applications of Data Science:

Data Science finds extensive application across diverse industries such as banking, consultancy, healthcare, and manufacturing.

Data Science Integration in Business:

Data Science can be seamlessly integrated into various facets of business operations where relevant data is available, including.

Role of a Data Scientist:

A Data Scientist requires expertise in several key disciplines:

  • Machine Learning: Utilizing algorithms to uncover patterns and make predictions.
  • Statistics: Employing statistical methods to analyze and interpret data.
  • Programming (Python): Writing code for data manipulation and analysis.
  • Mathematics: Applying mathematical concepts for modeling and analysis.
  • Databases: Working with databases to extract, manage, and process data.

Data Scientists follow a systematic approach in their work:

  • Formulating pertinent questions to comprehend the business problem at hand.
  • Exploring and collecting relevant data from diverse sources, such as databases, web logs, and customer feedback.
  • Extracting and transforming the data into a standardized format for consistency and comparability.
  • Cleaning the data by eliminating erroneous values.
  • Identifying and addressing missing values by suitable replacements, such as averages.
  • Normalizing the data by scaling values to practical ranges for meaningful analysis.
  • Analyzing the data, identifying patterns, and making informed predictions.
  • Presenting the results with valuable insights in a manner that the organization can easily grasp and utilize.

Data Science stands as a pivotal discipline that empowers businesses and industries to harness the full potential of their data. As you embark on your Data Science journey, I wish you the best of luck in your endeavors. May your pursuit of knowledge and insights be fruitful, and may you find innovative solutions to complex challenges.

Remember that your dedication and expertise as a Data Scientist can drive meaningful impact, enabling organizations to make informed decisions, predict future trends, and unlock the hidden value within their data. Best of luck on your path to becoming a proficient Data Scientist!

Who this course is for:

  • Person who have Intrinsic Intellectual Curiosity & Machine Learning
  • Person who have Startup and Business Goals
  • Person who Developing Data-Driven Marketing
  • Person who have Interest and Ability in Coding
  • Person who have Analytical Skills and Expertise in Mathematics

What you will learn

After completing this course, a trainee will be able to:

  • Complete Python Programming, Python Numpy, Python Matplotlib, Scikit-Learn, Data Analysis
  • Machine Learning With Python, Python Scipy, Practise Papers and Quizes, Real Life Projects, K-nearest neighbors (KNN)
  • Mean Median Mode, Standard Deviation, Data Distribution, Normal Data Distribution, Scatter Plot
  • Linear Regression, Polynomial Regression, Multiple Regression, Scale, Train/Test, Confusion Matrix
  • Hierarchical Clustering, Logistic Regression, Grid Search, Categorical Data, K-means, Cross Validation
  • Python Data Types, Python Lists, Python Tuples, Python Sets, Python Dictionaries, Python If ... Else
  • Python While and for Loops, Python Arrays, Python Classes and Objects, Python Modules, Python Iterators, Python Try Except
  • NumPy Data Types, NumPy Array Iterating, NumPy Random, Random Data Distribution, Pandas DataFrames, Pandas - Analyzing DataFrames