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- What is Data Science?
- What is Big Data?
- What is Machine Learning?
- What is Analytics?
- What is Data Analysis and Data Mining?
- Analytics project life cycle
- Real life applications, projects, and career paths of Data Science and Big Data
- Definition and computational probability
- Measurement of central tendencies and their applications
- Spreads, Distributions(Normal, Z distribution, Binomial, Poisson), and various types of probability distributions(Continuous and discrete)
- Sampling and Sampling distributions
- Measures of shape( Skewness and Kurtosis)
- Measures of the relationship between variables(Correlation, causation)
- Hypothesis Testing(t-test, Chi-square, Anova)
- Measures of Dispersion( Variance, std. deviation, Range)
- Prediction and Confidence interval Computation and
- Analysis Missing Value theorem
3. Exploratory Data Analysis(EDA) and Data Visualization
- What is EDA and why is it required?
- Outlier treatment
- Data distributions and transformations
- Graphs, Bar charts, Histogram
- Box-Whisker plot, Scatter plot
- Variable selection, Bubble charts
4. Data processing using MS Excel:
- Inbuilt functions
- Lookup tables
- Rank determination
- Conditional formatting
- Data Validation
- Pivot tables
5.Data manipulation using SQL
- Introduction to SQL and Databases
- SQL developer installation
- Data types
- Data types and Operators
- Create and Drop database
- DDL, DML, DCL, TCL, Sorting commands, and other keywords
- Advanced SQL-Wild cards, Constraints, Joins, Unions, NULL, Alias, Truncate, Views, Subqueries
6.Introduction to R
- Why R and the importance of R in Analytics?
- Installation of R and R-studio
- Data types, Variables, Operators, Decision making
- Loops, Lists, Vectors, Strings, Matrices, Arrays, Factors
- Functions (Built-in and User-defined functions) (aggregate, subset, merge, apply, apply, as. XXXX, which, sort,order-mandatory)
- Importing Data from texts, spreadsheets, and web data
- Extracting Tweets from Twitter using API
- Data frames
- Packages, libraries, and their installation
- Data manipulation and re-shaping
- Data Visualization using R