Course Date: 01 Nov 2022- 23 Nov 2022
Data Science has become an essential part of everyday life and emerged as a critical component of modern business intelligence in recent years. Its algorithms are becoming ever necessary due to various factors like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.
According to Kdnuggets- Businesses will need one million ML engineer/ data scientists in the near future
Data Scientist has been named the best job – Forbes
The objective of the Course
- To familiarize the participants with basic learning algorithms, techniques & their applications, as well as general questions.
- Provide the foundation of Python scripting and its principles
- Design and implement real-world inspired ML and DL algorithms applications (Image Processing with MLP).
- Boost your career prospects through innovative and independent learning
- Project work with real-life problems (Handwritten optical image recognition and Exploratory analysis on Covid 19)
Benefits and Outcomes of the Course
- 40+ Hours live instructor-led classes.
- Special Lectures from Industry and Academia Exports
- Hands-on sessions from Industry experts
- Career guidance sessions from the experts.
- 3 hrs of doubt clearing sessions on weekends.
- Access to learning material and video lectures.
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Python Objects, Numbers & Booleans, Strings, Container objects, Mutability of objects Concept of Packages/Libraries – Important packages (Pandas, NumPy, SciPy, Scikit-learn, Seaborn, Matplotlib)
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Operators – Arithmetic, Bitwise, Comparison and Assignment Operators
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Conditions
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Loops
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Break and Continue Statement
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Range Functions
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Introduction exploratory data analysis
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Descriptive statistics, Frequency Tables and summarization
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Univariate Analysis (Distribution of data & Graphical Analysis)
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Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
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Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
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Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas and SciPy. stats etc)
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