Accelerate Your Career with
Data Science Mastery

Python

This module introduces you to the basics of UI and UX design, focusing on how they come together to create user-friendly digital products. You’ll learn key concepts that form the foundation of designing great user interfaces and experiences.

• Python Introduction
• Data Types
• Collections
• Creating a List
• Control Flow Statements
• Conditional Statements
• Loops in Python
• Control Transfer Statements
• Functions in Python
• Built-in Functions
• User Defined Functions
• File Handling
• Exception Handling
• OOPS in Python
• Class and Objects
• Inheritance
• Polymorphism
• Encapsulation
• Abstraction
• Python for Data Science
• Numpy
• Arrays
• Array operation
• Indexing and slicing
• Shape and reshape
• Data types
• Mathematical functions
• Statistical functions
• Random
• Pandas
• Data structures
• Data operations
• Data manipulation

SQL

In this module, you’ll dive into the process of analyzing user needs and behaviors to create better design solutions. We’ll explore how data-driven insights can inform your design decisions, making your projects more user-focused.

• Why DataBase
• Types of DataBase
• Why Postgre SQL
• Installation
• SQL Languages
• Data Definition Language (DDL)
• Data Manipulation Language (DML)
• Data Query Language (DQL)
• Data Control Language(DCL)
• Transaction Control Language (TCL)
• Constraints
• Clauses of SELECT statement
• Subquery
• JOINS
• Windows Function
• Aggregate function
• UNION/UNION ALL
• Normalization
• Special Operators
• Stored Procedure

Introduction to Data Science

This module covers the key methodologies and principles behind effective UI/UX design. You’ll learn about different design approaches and how core principles guide the creation of intuitive and accessible digital products.

Real -World applications

• Finance
• Healthcare
• Marketing etc.,

Data Science Workflow

• Data collection
• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Model building
• Model evaluation and deployment

Data Collection and Preprocessing

In this module, you will learn the importance of thorough research in the UI/UX design process. We’ll cover methods to gather user insights and how to apply them to create user-focused and effective designs.

• Data Collection Techniques
• Data Cleaning and Wrangling
• Handling missing values (deletion and imputation)
• Managing outliers
• Transforming data (scaling and normalization)
• Merging and combining datasets
• Handling Missing Data
• Understanding missing data patterns
• Techniques for handling missing data
• Assessing the impact of missing data on analysis
• Feature Engineering
• Creating new features from existing data
• Binning and categorizing data
• Scaling and normalizing features
• Encoding categorical variables

Data Visualization

This module introduces you to the essential tools and software used in the UI/UX design process. You’ll get hands-on experience with industry-standard tools to bring your design ideas to life.

• Principles of Data Visualization
• Importance of visualization in data analysis
• Best practices for effective visualizations
• Choosing the right chart type
• Visualization Tools
• Matplotlib, Seaborn (Python)
• ggplot2
• Customizing plots (colors, labels, legends)
• Creating Basic Plots
• Bar charts
• Line charts
• Scatter plots
• Histograms
• Advanced Visualization Techniques
• Heatmaps
• Pair plots
• Box plots and violin plots

Probability and Statistics for Data Science

In this module, you’ll explore the core elements of visual design that bring your UI/UX projects to life. You’ll learn how to create aesthetically pleasing and user-friendly designs that enhance user interactions.

• Descriptive Statistics
• Measures of central tendency (mean, median, mode)
• Measures of dispersion (variance, standard deviation)
• Skewness and kurtosis
• Probability Theory
• Basic probability concepts
• Conditional probability and Bayes’ Theorem
• Probability Distributions
• Normal distribution
• Binomial distribution
• Poisson distribution
• Probability density functions (PDFs) and cumulative
• Distribution functions (CDFs)
• Hypothesis Testing
• Null and alternative hypotheses
• Types of errors (Type I and Type II)
• p-values and statistical significance
• t-tests, chi-square tests, ANOVA
• Confidence Intervals and P-Values
• Constructing confidence intervals
• Interpreting p-values
• Margin of error

Exploratory Data Analysis (EDA)

This module focuses on the basics of wireframing, an essential step in UI/UX design. You’ll learn how to create clear, functional layouts that form the blueprint of your digital products.

• Importance of EDA
• Why EDA is crucial in the data science process
• Understanding data distributions and patterns
• Identifying data quality issues
• Techniques for EDA
• Univariate, bivariate, and multivariate analysis
• Data summarization and visualization
• Correlation analysis
• Correlation and Causation
• Understanding correlation coefficients
• Spurious correlations
• Causation vs. correlation
• Dimensionality Reduction Techniques
• Principal Component Analysis (PCA)
• Handling high-dimensional data

Introduction to Machine Learning

In this module, you’ll learn how to evaluate the effectiveness of your UI/UX designs through user testing. We’ll cover different testing methods to ensure your designs meet user needs and provide a seamless experience.

• Supervised vs. Unsupervised Learning
• Differences between supervised and unsupervised learning
• Examples of each type
• Key algorithms
• Regression Techniques
• Linear regression
• Polynomial regression
• Assumptions of linear regression
• Evaluating regression models
• Classification Techniques
• Logistic regression
• K-Nearest Neighbors (KNN)
• Decision boundaries
• Evaluating classification models
• Introduction to Model Evaluation Metrics
• Accuracy, Precision, Recall, F1-score
• Confusion matrix
• ROC and AUC curves
• Cross-validation technique

Advanced Machine Learning

This module dives deeper into Figma’s advanced features, helping you take your design skills to the next level. You’ll explore how to use Figma for more complex projects and optimize your workflow.

• Decision Trees and Random Forests
• Building decision trees
• Overfitting and pruning
• Random Forests and Feature Importance
• Bagging techniques
• Support Vector Machines
• SVM for classification
• Kernel methods
• Hyperparameter tuning
• Ensemble Methods
• Bagging vs. boosting
• Gradient Boosting Machines (GBM)
• XGBoost, AdaBoost
• Hyperparameter Tuning
• Grid search and random search
• Cross-validation in hyperparameter tuning
• Automating hyperparameter tuning
• Model Overfitting and Regularization Techniques
• Understanding overfitting and underfitting
• Regularization methods (Lasso, Ridge)
• Bias-variance tradeoff

Unsupervised Learning

In this module, you’ll explore additional skills that complement your UI/UX design expertise. These skills will enhance your overall design process and make you a more versatile designer.

• Decision Trees and Random Forests
• Building decision trees
• Overfitting and pruning
• Clustering Techniques
• K-Means clustering
• Hierarchical clustering
• Evaluating clustering results
• Dimensionality Reduction in Unsupervised Learning
• Application of PCA in unsupervised learning
• Latent Dirichlet Allocation (LDA)
• Association Rule Learning
• Market basket analysis
• Apriori algorithm
• Evaluation metrics (Support, Confidence, Lift)
• Anomaly Detection
• Techniques for identifying anomalies
• Applications in fraud detection
• Isolation Forest, DBSCANBagging techniques
• Support Vector Machines
• SVM for classification
• Kernel methods
• Hyperparameter tuning
• Ensemble Methods
• Bagging vs. boosting
• Gradient Boosting Machines (GBM)
• XGBoost, AdaBoost
• Hyperparameter Tuning
• Grid search and random search
• Cross-validation in hyperparameter tuning
• Automating hyperparameter tuning
• Model Overfitting and Regularization Techniques
• Understanding overfitting and underfitting
• Regularization methods (Lasso, Ridge)
• Bias-variance tradeoff

Deep Learning

In this module, you’ll explore additional skills that complement your UI/UX design expertise. These skills will enhance your overall design process and make you a more versatile designer.

• Deep Learning Frameworks
• Introduction to TensorFlow
• Introduction to Keras
• Building basic neural networks
• Convolutional Neural Networks (CNNs)
• Convolutional layers, pooling layers
• CNN architecture for image classification
• Transfer learning in CNNs
• Recurrent Neural Networks (RNNs)
• RNNs for sequence data
• Long Short-Term Memory (LSTM) networks
• Applications in time series forecasting and NLP
• Transfer Learning
• Using pre-trained models
• Fine-tuning and customizing models
• Applications in various domains

Natural Language Processing (NLP)

In this module, you’ll explore additional skills that complement your UI/UX design expertise. These skills will enhance your overall design process and make you a more versatile designer.

• Basics of NLP
• Introduction to NLP and text data
• Common NLP tasks (sentiment analysis, text classification)
• Text Preprocessing
• Tokenization, stop words, stemming, and lemmatization
• Bag of Words (BoW) and TF-IDF
• Word embeddings (Word2Vec, GloVe)
• Sentiment Analysis
• Techniques for sentiment analysis
• Applications in social media and marketing
• Text Classification
• Naive Bayes classifier
• SVM for text classification
• Evaluating NLP models
• Introduction to Transformers
• Overview of transformer models (BERT, GPT)
• Applications of transformers in NLP tasks
• Fine-tuning transformers for specific tasks

Power BI

In this module, you’ll explore additional skills that complement your UI/UX design expertise. These skills will enhance your overall design process and make you a more versatile designer.

• Introduction to Power BI
• Data Preparation
• Basic data cleaning and transformation
• Data Modeling
• Visualizations
• Basic charts and tables
• Bar Chart
• Stacked Bar chart
• Pie Chart
• Donut Chart
• Line Chart
• Area Chart
• Scatter Plot
• KPI (Key Performance Indicator)
• Tree Map
• Bubble Chart
• Matrix
• Table
• Funnel Chart
• Filled Map
• Gauge Chart
• Ribbon Chart
• Q & A Card
• Multi Card
• Slicer
• Combination Chart
• Customizing Visuals
• Building Interactive Dashboards
• Report Sharing
• Publishing to Power BI Service
• Sharing reports with others
• Advanced Features
• Power BI Q&A (natural language queries)
• Basic integration with other tools (e.g., Excel)