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SQL Fundamentals, Probability and Statistics course Data Processing and Visualisation

Location: work from home

JOB DESCRIPTION:

  • This course offers you an introduction to the database management required for data science.
  • TIt enables to employ Structured Query Language (SQL) to work with the MySQL database.
  • It focuses on integral processes of Database and SQL – managing MySQL database and tables, querying data stored in tables, and accessing database using Python commands.
  • Understand the concept of probability and its relation with mathematical sets
  • Be able to perform basic operations with sets and probabilities and use them to answer interesting questions
  • Contrast population and sample, parameter and its estimate
  • Understand and contrast the concepts of basic summary statistics, e.g., mean, variance, standard deviation, etc. for population versus samples and learn to estimate them from data
  • Understand the concept of effect size and uncertainty
  • Have a comprehensive idea about different kinds of probability distributions both discrete and continuous
  • Understand the concept of hypothesis, type I and II errors, level of significance, and power
  • Understand and be able to test hypotheses for mean and proportion and draw useful inferences from the process
  • Define and recognize the relation between covariance and Pearson’s correlation coefficient
  • Recognize the importance of dimensionality reduction, and comprehend principal component analysis. Along the way, you will learn about different matrix factorization techniques, eigenvalues, eigenvectors, their properties and interpretation
  • Understand data cleaning techniques.
  • Perform sorting and aggregation on DataFrames
  • Create visualizations to report results of data analysis.
  • Use different data normalization techniques based on the underlying data distribution.
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Education Required:

  • BE/ BTech students-any stream
  • Non-engineering students-STEM background
  • Working Professionals
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Tools you will

    • Data preprocessing
    • data cleaning
    • data types
    • pandas
    • DataFrames
    • Data normalization
    • plot, data visualization
    • python
    • matplotlib
    • Introduction to sets
    • Basic set operations, probability
    • Sum and multiplication ‘rules’ of probability
    • Probability distributions
    • Discrete and continuous probability distribution
    • Hypothesis testing
    • T and chi-squared tests
    • Covariance
    • Correlation
    • Principal component analysis
    • SQL
    • Queries
    • MySQL
    • DDL
    • SELECT
    • Database
    • RDBMS
    • DML
    •  

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