📊 Course Overview:

The Data Science Elite: Python, Power BI, SQL, and ML Mastery course is a comprehensive, instructor-led program crafted to launch your career as a high-performing data professional. Delivered through live, online classes by Microcare Academy, this course offers an immersive learning experience designed for immediate job readiness.

You'll dive deep into:

• 🐍 Python Programming for Data Analysis and Automation
• 🛢️ SQL for Database Queries and Data Management
• 📊 Power BI for Interactive Dashboards and Visual Analytics
• 🤖 Machine Learning & AI for Predictive Modeling
• 🔁 MLflow & MLOps for Model Deployment and Lifecycle Management
• 📈 Data Wrangling, Transformation, and Feature Engineering
• 🧠 Deep Learning with Neural Networks
• 🔍 Data-Driven Decision Making using Real-World Projects

Through hands-on training and real-world projects, you'll gain practical expertise in data manipulation, visualization, modeling, deployment, and performance monitoring.

💼 With dedicated placement support, this course is more than just education—it’s a career transformation platform, empowering you to thrive in today’s data-driven world.

Skills:

Tools

Course content

• MODULE 1: FUNDAMENTALS OF PYTHON12 lectures
  • What is Python?
  • Why is Python essential for Data Science?
  • Versions of Python
  • How to install Anaconda Distribution?
  • How to use Jupyter Notebooks?
  • Python Data Types
  • Operators, Conditional Statements, Loops
  • Lists, Tuples, Dictionaries, Sets
  • Methods and Functions
  • Errors and Exception Handling
  • Object-Oriented Programming
• MODULE 2: DATA HANDLING WITH NUMPY AND PANDAS 22 lectures
  • 2.1 Numpy
  • Introduction to NumPy
  • Creating arrays from Python lists and tuples
  • Standard data types
  • Two-dimensional arrays
  • Array Attributes
  • Array Indexing and Slicing
  • Array Creations, Manipulations, Aggregations, and Comparisons
  • Masks and Boolean Logic
  • 2.2 Pandas
  • Introduction to Pandas
  • Pandas data types
  • Data indexing and selecting
  • Input and Output
  • Combining and Merging
  • Aggregation and Grouping
  • Analyzing data with Pivot tables
  • String operations
  • Data transformation
  • Working with date and time columns
  • Conditional filtering
  • Getting information about data
  • Handling missing values
• MODULE 3: DATA VISUALIZATION WITH SEABORN 13 lectures
  • Overview of Seaborn
  • Why visualize data?
  • Plotting univariate histograms
  • Kernel density estimation
  • Empirical cumulative distributions
  • Visualizing bivariate distributions
  • Distribution visualization in other settings
  • Categorical scatterplots
  • Distributions of observations within categories
  • Statistical estimation within categories
  • Visualizing regression models
  • Multi-plot grids
  • Controlling figure aesthetics
  • Creating interactive dashboards with Streamlit for visualization
• MODULE 4: EXPLORATORY DATA ANALYSIS 11 lectures
  • What is EDA?
  • Steps involved in EDA
  • Importing data
  • Data pre-processing (overview)
  • Identifying inconsistencies in data
  • Identifying missing values
  • Merging multiple datasets
  • Subsetting data
  • Adding new features to data
  • Exploring and summarizing data
  • Developing insights from data
• MODULE 5: FUNDAMENTALS OF APPLIED STATISTICS 30 lectures
  • 5.1 INTRODUCTION TO STATISTICS
  • Definition of Statistics
  • Categories of Statistics: Descriptive and Inferential
  • Qualitative vs Quantitative Data
  • Scales of Measurements: Nominal, Ordinal, Interval, Ratio
  • Understanding data types in datasets (e.g., House sales, Churn)
  • Statistical Terms and Definitions
  • Population vs Sample
  • 5.2 DESCRIPTIVE STATISTICS
  • Measures of Central Tendency: Mean, Median, Mode, Weighted Mean, Geometric Mean, Harmonic Mean
  • Pros and cons of Mean and Median
  • Measures of Dispersion: Range, Interquartile Range, Mean Absolute Deviation, Variance, Standard Deviation, Z-score
  • Measures of Shape: Skewness and Kurtosis
  • 5.3 PROBABILITY AND PROBABILITY DISTRIBUTION
  • Definition of Probability and Properties
  • Counting rules
  • Conditional Probability
  • Bayes’ Rule
  • Introduction to Probability Distribution
  • Random Variables and Types
  • Types of Probability Distributions
  • Discrete Distributions: Binomial, Poisson, Uniform
  • Continuous Distributions: Normal
  • 5.4 INFERENTIAL STATISTICS
  • Introduction to Inferential Statistics
  • Point vs Interval Estimations
  • Confidence Interval
  • Sampling Distribution and Standard Error
  • Central Limit Theorem
  • Hypothesis Testing
  • Steps for Hypothesis Testing
  • Null and Alternative Hypotheses
  • Type-1 and Type-2 Errors
  • Level of Significance, Critical Value, P-value
  • T-tests: One-sample, Two-sample, Paired
  • F-test for Variance Comparison
  • Chi-square Test for Goodness of Fit
• MODULE 6: DATA PREPROCESSING 14 lectures
  • Importance of Data Preprocessing in Data Science
  • Data Cleaning: Identifying and Correcting Inconsistencies
  • Handling Missing Values: Deletion, Imputation (Mean, Median, Mode)
  • Handling Outliers: Detection and Treatment
  • Data Integration: Merging and Joining Datasets
  • Data Transformation: Normalization, Standardization
  • Encoding Categorical Variables: One-Hot Encoding, Label Encoding
  • Feature Scaling: Min-Max Scaling, Standard Scaling
  • Feature Engineering: Creating New Features
  • Handling Imbalanced Data: Oversampling, Undersampling, SMOTE
  • Text Preprocessing for NLP: Tokenization, Stop Words Removal, Lemmatization
  • Data Reduction: Sampling, Binning
  • Using Pandas and Scikit-learn for Preprocessing
  • Pipeline Creation with Scikit-learn for Reproducible Workflows
• MODULE 7: INTRODUCTION TO MACHINE LEARNING 4 lectures
  • What is Machine Learning?
  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement
  • Machine Learning Process
  • Introduction to MLFlow for Experiment Tracking
• MODULE 8: REGRESSION 9 lectures
  • Introduction to Regression
  • Relationship between Variables
  • Simple Linear Regression
  • Multiple Linear Regression
  • Creating Regression Models
  • Evaluation of Regression Models
  • Multicollinearity
  • Variance Inflation Factor
  • Lasso and Ridge Regression
  • Tracking Regression Experiments with MLFlow
• MODULE 9: CLASSIFICATION 10 lectures
  • Introduction to Classification
  • Logistic Regression
  • Creating Logistic Regression Models
  • Estimating Probabilities
  • Evaluation of Classification Models
  • Confusion Matrix
  • F1 Score
  • K-Nearest Neighbours for Classification
  • Naive Bayes Algorithm
  • Tracking Classification Experiments with MLFlow
• MODULE 10: NATURAL LANGUAGE PROCESSING 7 lectures
  • Introduction to NLP
  • Libraries for NLP (NLTK, SpaCy)
  • Text Mining Process
  • Feature Engineering
  • Text Preprocessing and Normalization
  • Word Embedding or Vectorization
  • Deploying NLP Models with Streamlit
• MODULE 11: DECISION TREE 5 lectures
  • Introduction to Decision Trees
  • Training and Evaluating
  • Decision Trees for Classification and Regression
  • Gini Impurity or Entropy
  • Regularization Hyperparameters
• MODULE 12: ENSEMBLE LEARNING 9 lectures
  • What is Ensemble Learning?
  • Types of Ensemble Learning Methods
  • Bias and Variance
  • Bagging and Boosting
  • How Random Forest Works
  • AdaBoost
  • Gradient Boosting
  • Feature Importance with Random Forest
  • Out-of-Bag Evaluation
• MODULE 13: DIMENSIONALITY REDUCTION 7 lectures
  • The Curse of Dimensionality
  • Types of Dimensionality Reduction
  • Feature Selection vs Feature Extraction
  • Backward Elimination vs Forward Selection
  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Explained Variance Ratio
• MODULE 14: UNSUPERVISED LEARNING 8 lectures
  • Introduction to Clustering
  • Applications of Clustering
  • K-Means Clustering
  • Performing K-Means Clustering
  • Within-Cluster Sum of Squares
  • Determining the Value of K (Elbow Method, Silhouette Method)
  • Hierarchical Clustering
  • Density-Based Clustering
• MODULE 15: DEEP LEARNING 11 lectures
  • Introduction to Deep Learning
  • Neural Networks: Perceptrons, Multi-Layer Perceptrons (MLP)
  • Activation Functions: Sigmoid, ReLU, Tanh
  • Deep Learning Frameworks: TensorFlow, Keras, PyTorch
  • Convolutional Neural Networks (CNNs) for Image Data
  • Recurrent Neural Networks (RNNs) for Sequential Data
  • Training Deep Learning Models: Backpropagation, Gradient Descent
  • Regularization Techniques: Dropout, L2 Regularization
  • Preprocessing Data for Deep Learning
  • Tracking Deep Learning Experiments with MLFlow
  • Deploying Deep Learning Models with Streamlit
• MODULE 16: HYPERPARAMETER TUNING 10 lectures
  • Introduction to Hyperparameters
  • Importance of Hyperparameter Tuning
  • Common Hyperparameters in Machine Learning Models
  • Manual Tuning vs Automated Tuning
  • Grid Search
  • Random Search
  • Bayesian Optimization
  • Using Scikit-learn and Optuna for Hyperparameter Tuning
  • Tracking Tuning Experiments with MLFlow
  • Evaluating the Impact of Hyperparameter Tuning on Model Performance
• MODULE 17: MODEL EVALUATION AND VALIDATION 10 lectures
  • Importance of Model Evaluation
  • Train-Test Split
  • K-Fold Cross-Validation
  • Stratified K-Fold for Imbalanced Data
  • Performance Metrics for Regression: MSE, RMSE, MAE, R²
  • Performance Metrics for Classification: Accuracy, Precision, Recall, F1 Score, ROC-AUC
  • Overfitting and Underfitting
  • Learning Curves and Validation Curves
  • Using Scikit-learn for Model Validation
  • Logging Evaluation Metrics with MLFlow
• MODULE 18: MLOPS AND MODEL DEPLOYMENT 9 lectures
  • Introduction to MLOps
  • MLOps Lifecycle: Data, Model, Deployment, Monitoring
  • Using MLFlow for Model Management and Deployment
  • Model Versioning and Registry
  • Continuous Integration and Deployment for ML
  • Monitoring Model Performance
  • Building and Deploying Data Apps with Streamlit
  • Containerization with Docker for ML Models
  • Scalable Model Deployment with Cloud Platforms
• MODULE 19: POWER BI 10 Topics + Projects
  • INTRODUCTION TO POWER BI
  • DATA EXTRACTION / CONNECTION
  • DATA TRANSFORMATION – SHAPING & FILTERING
  • DATA TRANSFORMATION – DATA CLEANING
  • DATA TRANSFORMATION – DATA MASHUP
  • DATA MODELLING
  • DAX
  • DATA VISUALIZATION WITH ANALYTICS
  • POWER BI SERVICE (Cloud), Q & A AND DATA INSIGHTS
  • REAL TIME PROJECTS
• MODULE 20: PROJECTS 6 lectures
  • End-to-End Data Science Projects
  • Incorporating Data Preprocessing Pipelines
  • Using MLFlow for Experiment Tracking and Model Management
  • Hyperparameter Tuning for Optimized Models
  • Deploying Project Dashboards with Streamlit
  • Example Projects: Predictive Modeling, NLP Applications, Clustering Analysis, Deep Learning Applications

Career Progression and Salary Trends

Learning Path