Data Science Full Stack Course Syllabus
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Our in-depth full stack data science course syllabus is intended to provide you the highly sought-after abilities of a Full Stack Data Scientist. Delve into the useful facets of data engineering, data visualization, and implementing data science solutions and acquire a solid foundation in fundamental data science principles, programming languages, and machine learning techniques. Our full stack data science syllabus covers core data science concepts, machine learning algorithms, data engineering concepts, big data technologies and tools like Hadoop, Spark, data visualization tools like Tableau, DevOps technologies like Docker and Kubernetes, and so on.
Course Syllabus
Download SyllabusCORE PYTHON
- Python Introduction & history
- Color coding schemes
- Salient features & flavors
- Application types
- Language components (variables, literals, operators, keywords…)
- String handling management
1. String operations – indexing, slicing, ranging
2. String methods – concatenation, repetition, formatting
3. Supporting functions - Native data types
1. List
2. Tuple
3. Set
4. Dictionary - Decision making statements
1. If
2. If…else
3. If…elif…else - Looping statements
1. For loop
2. While loop - Function types
1. Built-in functions
2. Math functions
3. User defined functions
4. Recursive functions
5. Lambda functions - OOPs
1. Classes and objects
2. __init__ constructor
3. Self-keyword
4. Data abstraction
5. Data encapsulation
6. Polymorphism
7. Inheritance - Exception handling
1. Error vs exception
2. Types of error
3. User defined exception handling
4. Exception handler components
5. Try block, except block, finally block - File handling
1. How to create a txt file using python
2. File access modes
3. Reading and writing data to a txt file
4. Data operations
DATA SCIENCE PHASE 1
- Working with PANDAS & NUMPY
1. PANDAS – data analysis intro
2. PANDAS – data structures
3. Series creation types
4. Data Frame creation types
5. Accessing data from Series and DataFrame
6. Data merging - Working with PANDAS & NUMPY
1. Data mapping
2. Finding duplicates
3. Removing duplicates
4. Describing data
5. Finding null values
6. Group by function
7. Sort values
8. Statistical functions
9. Reading and writing data from CSV
10. Data operations on CSV file
11. Basic visualizations
12. NUMPY array processing intro
13. Types of ndarray - Numpy attributes
1. ndim
2. shape
3. size
4. type - Shape manipulations
1. Ravel
2. Reshape
3. Resize
4. Hsplit
5. Vstack - Numpy additional functions
1. Tile
2. Eye
3. Zeros
4. Ones
5. Diag
6. arange
7. New axis addition
8. Random number generation
DATA SCIENCE PHASE 2
- Data science terminologies
- Exploratory data analysis intro
- Types of machine learning algorithms
- Classification and regression intro
- Prediction and analysis techniques to be used in ML
- MATPLOTLIB – data visualization
1. Histogram
2. Pdf
3. Adding axes
4. Adding grid
5. Adding label
6. Adding ticks
7. Setting limits
8. Adding legend - MATPLOTLIB plotting
1. Bar chart
2. Pie chart
3. Heat map
4. Box plot
5. Scatter plot
6. 3d plot - SEABORN – advanced color palette visualization
1. Bar chart
2. Pie chart
3. Dist plot
4. Pair plot
5. Reg plot
6. Count plot
7. Swarmplot
8. Heat map
9. Scatter plot
10. Lm plot
EDA – MACHINE LEARNING –WORKING WITH SCIKIT-LEARN
- Machine learning algorithm types
1. Supervised learning
2. Unsupervised learning
3. Ensemble learning technique - Working flow of dataset
1. Loading necessary modules
2. Loading dataset
3. Feature scaling
4. Feature extraction
5. Data standardization
6. Data normalization
7. Data manifesting
8. Model creation
9. Fitting data models
10. Model prediction - ML algorithms with live demo and mathematical intuition
1. Linear regression
2. Logistic regression
3. Naïve bayes classifier
4. KNN (K nearest neighbor)
5. KMC (K means clustering)
6. Support vector machines
7. Principal component analysis
8. Decision tree
9. Random forest
10. XGBoost
DEEP LEARNING & AI
- Neural networks introduction
- Brain activation functions and layer components
- Neural network terminologies of ANN, CNN, RNN
1. Models
2. Initializers
3. Optimizers
4. Layers
5. Activation functions
6. Loss functions
7. Metrics
8. Model compilations
9. Model evaluation
10. Max pooling layers
11. Edge filters
12. Back propagations
13. Early stopping
14. Epoch - Datasets to be used for MLP,ANN, CNN,RNN
1. Boston house prediction
2. CIFAR10
3. CIFAR100
4. MNIST
5. FASHION MNIST
6. IMDB Movie review analysis - NLP (Natural Language Processing)
1. NLTK
2. NLTK
3. SPACY - COMPUTER VISION
1. Digital Image Processing using CV2 library
2. LIVE PROJECTS
Get acquainted with programming concepts, mathematics, statistics, TensorFlow, PyTorch, AWS, analytical and problem-solving, and so on through our full stack data science syllabus.
