Data Science Bundle
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What you will get when you take this course:
- World-class course content taught live by the expert Dr R Nageswara Rao.
- Zoom link for the class daily from course start to end date to your mail.
- Opportunity to clarify your doubts at the end of each class.
- Placement Assistance by posting available jobs, exclusively for Datatechs students.
- Certificate of Completion upon successful completion of the course.
Data Science bundle consists of 8 Modules:
- FUNDAMENTALS OF SQL
- PYTHON FOR DATA SCIENCE
- STATISTICAL METHODS
- POWER BI
- MACHINE LEARNING IN AI
- DEEP LEARNING IN AI
- NATURAL LANGUAGE PROCESSING (NLP) IN AI
- COMPUTER VISION IN AI
Data Science Syllabus
MODULE 1: FUNDAMENTALS OF SQL
- DBMS
- DBMS vs RDBMS
- SQL
- Installation of Oracle / MySQL
- DDL
- DQL
- DML
- DCL
- TCL
- Arithmetic Operators
- Relational Operators
- Logical Operators
- Primary key
- Foreign key
- Composite key
- Inner Join
- Left Join
- Right Join
- Outer (Full) Join
- ORDER BY
- GROUP BY
- Aggregate functions in SQL
- SELECT in Sub queries
- INSERT in Sub queries
- UPDATE in Sub queries
- DELETE in Sub queries
- Creating view from a table
- Creating view from multiple tables
- Updating data into a view
MODULE 2: PYTHON FOR DATA SCIENCE
- Advantages of Python
- Python compiler and PVM
- Python instillation and environment
- strings
- char
- lists
- tuples
- range
- sets
- dictionaries
- if statement
- if…else statement
- if…elif…else statement
- while loop
- for loop
- break statement
- continue statement
- pass statement
- Array creation
- Array attributes
- 1D and 2D Arrays
- Matrix
- Built in and User defined functions
- Writing your own functions
- Importing functions
- Modules
- Packages
- Imports
- Classes and Objects
- Encapsulation
- Abtraction
- Inheritance
- Polymorphism
- Matching strings
- Searching for strings
- Finding all strings
- Splitting a string into pieces
- Replacing strings
- Test files
- Binary files
- Connecting to Oracle database
- Connecting to MySQL database
- Series
- Dataframes
- Creation of dataframes from different sources
- Viewing data in dataframe
- Operations on dataframe
- Handling missing data
- Selecting rows, columns
- Grouping the rows
- Regular expressions in pandas
- Window functions in pandas
- Aggregate functions in pandas
- Joining the dataframes
- Date Time Indexing
- Line plot
- Bar graph
- Pie chart
- Subplots
- Histogram
- Distribution plot
- Kde plot
- Count plot
- Box plot
- Scatter plot
- Sub plots
- Lmplot
- Pair plots
MODULE 3: STATISTICAL METHODS
- What is statistics?
- Types of statistics
- Descriptive statistics
- Inferential statistics
- Population
- Sample
- Variable (discrete and continuous)
- Data and types of data
- Qualitative (nominal and ordinal)
- Quantitative (interval scale and ratio scale)
- Mean
- Median
- Mode
- Probability with replacement
- Probability without replacement
- Probability Mass Function (PMF)
- Probability Density Function (PDF)
- Skewness
- Kurtosis
- variance
- std
- percentile
- quartile
- range
- IQR
- Empirical Rule
- Problems on Empirical Rule
- Chebyshev’s Theorem
- Normal distritution
- Standard normal distribution
- Sampling distribution of sample means
- Central limit theorem
- T- Distribution
- Student T- Test
- Chi Square Test (Goodness of Fit)
- Binomial distribution
- Bernoulli distribution
- Geometric distribution
- Hypergeometric distribution
- Poisson distribution
- Upper tail test
- Lower tail test
- Two tail test
- 1-way ANOVA
- 2-way ANOVA
MODULE 4: POWER BI
- Power BI Architecture
- Power Query Editor
- Power Pivot
- Power View
- Power Service
- Power BI Desktop installation
- Data types
- Sorting data
- Filtering data
- Transformations on columns
- M - language
- Creating different tables
- Joining the tables
- Fuzzy matching
- Grouping data
- Extracting data
- Statistical calculations
- Pivoting a column
- Adding new columns
- Query dependencies
- Tasks done in Power Pivot
- Different types of schemas
- Different types of relationships
- DAX calculation types
- Measures
- calculated columns
- DAX operators
- Text Functions
- Logical Functions
- Date and Time Functions
- Filter Functions
- Math & Statistical Functions
- Time Intelligence Functions
- Slicer
- Pie chart
- Donut chart
- Treemap
- Pie chart
- Donut chart
- Different types of bar charts
- Column charts
- Scatter chart
- Line chart
- Area chart
- Line and Stacked column chart
- Ribbon chart
- Waterfall chart
- KPI charts
- Gauge chart
- Funnel chart
- Visualizing geographical data
- Table visual
- Matrix visual
- Buttons creation
- Q&A text box
- Difference between dashboard and report
- Power BI Service
MODULE 5: MACHINE LEARNING IN AI
- One hot encoding using dummy variables
- One hot encoding using One hot encoder
- Simple Linear regression
- Multiple Linear regression
- Polynomial Linear regression
- Ridge regression
- Bias and Variance tradeoff
- Lasso regression
- Elasticnet regression
- Logistic regression
- Naive Bayes (Gaussian NB and Multinomial NB)
- KNN Classifier
- SVM
- Regularization
- Kernel Trick
- Decision Tree
- Entropy
- Gini Index
- Random Forest
- Conusion Matrix
- Bootstrapping, Bagging and Boosting
- K-Means Clustering
- Elbow technique
- Apriori Algorithm
- Different types of pipe lines
- ML Pipe line construction
- Eigen Vectors and Eigen Values
- Covariance
- Selecting appropriate model for our data
MODULE 6: DEEP LEARNING IN AI
- Biological Neural Network
- Artificial Neural Network
- Perceptrons
- Layers of a Network
- Identity Function
- Binary step function or Threshold function
- Logistic function or Sigmoid function
- ReLU function
- Hyperbolic Tangent function
- Softmax function
- ANN
- ANN with Activation functions
- Variables
- Constants
- Placeholders
- Graph / Tensor / Session
MODULE 7: NATURAL LANGUAGE PROCESSING IN AI (NLP)
- Tokenization
- Stemming
- Lemmatization
- Stop words
- POS
- CountVectorizer
- Tf-idf Vectorizer