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
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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