Data Science Full Stack Developer Course Syllabus

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This Data Science Full Stack course covers a comprehensive syllabus designed to equip you with the skills required for success in the field. It includes topics like foundations of data science, statistical analysis, machine learning, deep learning, big data technologies, web development, data visualization, database management, cloud computing fundamentals, etc. By the end of our Data Science Full Stack training at SLA, you will have the skills and hands-on exposure to tackle complex data science projects, from data acquisition and analysis to model development and deployment in IT services. So, enroll in our Data Science Full Stack Training to master Data Science Full Stack easily.

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Data Science Full Stack Course Syllabus

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CORE 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

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I am from Mechanical background. I have no command over programming before joining SLA Such a wonderful place to learn and achieve your goals.Having wonderful…