Become an expert in
Design Thinking and UI/UX

Introduction to Empathy, Why to Empathize, Understand the Market, User, Technology and Perceived Constraints, Technique to Empathize, User Research, Interviews, Surveys, Observations, Shadowing, User Personas, User Needs & goals, Creating User Personas, Empathy Mapping, Cultural Sensitivity, Conducting User Research, User Engagement Technique, Empathic Communication, Synthesize the findings from user research.

Design Principles (aesthetic, functional and ethical), Input & Start Point of Deisgn, gathering input from stakeholders, concept of design space, Explore and Generate Ideas, physical workspaces and digital collaboration tools, Idea Generation, Brainstorming, Mind Mapping, SCAMPER, Evaluating Ideas, Problem Identification and Definition, Problem Statements, Reframing, Problem Solving Techniques, Root Cause Analysis, FishBone Diagrams, Decision Tress & Risk Analysis to make effective decisions, Identify & Prioritize Constraints, discovering new opportunities & ideas.

User-Centered Design Process, Conceptualization & Ideation, User’s Mental Model, Products Designed on User’s Mental Model, Confused Mental Models, Products Designed on Confused Mental Model, UCD Process, Persona Mapping, Story Boarding, Scenario Map, Empathy Mapping. Methods of UX research – Qualitative / Quantitative, Data Gathering Methods & Sources, Expert Review, Interviews, Surveys & Email Questionnaire, Observation – Eye Tracking, Clickstream Analysis, Focus Group, User Groups, Market Segments, Competitor Analysis, User Stories, Task Flow, Information Architecture.

Cognitive Studies for Better User Experience, Gestalt Principles, Applying gestalt principles to UI/UX, Gestalt Principles in Web Design, Gestalt Principles in Mobile App Design, Examples of Gestalt Principles in Action, Microcopy, Visual Design, Color Theory, Tools for Designing with Color, Typography, How Typography Determines Readability, Combining Fonts, User Interface Elements, Using Graphics & Illustrations to Finalize Designs, Informational Components, Containers, UI Controls / Patterns, Input Controls, Navigational Components, Accessibility in Design, Types of Disabilities.

Introduction to Prototyping & Wireframing, sketching techniques, creating reusable design components, Fidelity of a Prototype - Low / Medium / High, Paper Prototyping, Digital Prototype, HTML Prototype, Sketching Techniques, Grid & Layout Systems, Interaction Design, tools & techniques for interactive prototyping, Organization Schemes, Information Design, Navigation, Flat vs Deep Hierarchies, Associative Navigation, creating & presenting a comprehensive prototype.

Introduction to Heuristic Evaluation, Assessing Prototypes, Writing Good Heuristic Evaluations, Visibility of System Status, User Control and Freedom, Engagement Levels, Error Tolerance, Aesthetic & Minimalist Design, Planning & Conducting Usability Test, Surveys and E-mail Surveys, Survey Tools, A/B Testing, UX Laws, Design Validation & Tradeoffs, Heatmaps with Tools like Hotjar User Testing.

Introduction to Figma, Figma Design File, Shapes, Selection, Typography, Design Tree, First Design, Colors, Drawing Tools, Margin, Padding, AutoLayout, Formatting Principles, Figma Constraints, Website Design, Styles and Components, Component Variants, Layout Grids & Design, Responsive Design, Material Design, Tailwind UI, Designing Mobile Apps, Iconography, Boolean Groups, Figma Token, Animations.

At the end of training, you need to go through with some design projects and exercises to increase your productivity and to build your experience.

Introduction to Dimensionality, Curse of Dimensionality, Dimensionality Reduction, Techniques of Dimensionality Reduction, Introduction to Principal Component Analysis (PCA), Dimensionality Reduction with PCA, Working with Dimensional Data, Problem Demonstration. Introduction to Linear Discriminant Analysis (LDA), Working of LDA, LDA & PDA comparison, other techniques for dimensionality reduction, missing value ratio, low variance filter, random forest, high correlation filter, Problem Demonstration. Introduction to Unsupervised Learning, Process Flow & Example, Clustering, types of clustering (exclusive, overlapping & hierarchical), K-Means Clustering Algorithm, Elbow Method, Applying K-Means Algorithm on 2D plots, Problem Demonstration. Introduction to Fuzzy C-Means Clustering, Problem Demonstration, DBSCAN (Density Based Spatial Clustering of Application with Noise) clustering algorithm, Problem Demonstration. Introduction to Association Rule Mining, Parameters (Support, Confidence, Lift), Generating Association Rules, Apriori Algorithm, Market Based Analysis, Problem Demonstration. Introduction to Recommendation System, Cosine-Based Similarity, Coverage, Common types of Recommender System, User Based Collaborative Filtering (UBCF), Content Based Filtering (CBF), User Driven Content and Service, Recommending similar movie to the user. Introduction to Time Series Analysis, Time Series Components (Trend, Seasonality, Cyclical Patterns, & Irregularity), Forms of Data (Stationary Data & Nonstationary Data), methods to check for stationary of data, Augmented Dicky-Fuller (ADF) Test, converting nonstationary data to stationary data, AutoCorrelation Function (ACF) and Partial AutoCorrelation Function (PACF), Auto Regression Model, Moving Average Model, Autoregressive Moving Average (ARMA) Model, ARIMA Model, Problem Demonstration & Case Studies.

Introduction to Model Selection, Resampling Techniques for Model Selection, Resampling Measures, K-Fold Cross Validation, Introduction to Model Evaluation, Problem Demonstration. Model Evaluation Metrics for Regression, Model Evaluation Metrices for Classification, Test Statistics, Confusion Matrix, Calculating Confusion Matrix, Problem Demonstration. Introduction to ROC Curve, Understanding the operation of ROC, Plotting ROC Curve, AUC Curve Operation, Problem Demonstration, Introduction to Precision and Recall, F1 Score, Problem Demonstration. Introduction to Hyperparameter Tuning, Types of Hyperparameter Optimization, Manual Search, Grid Search, Random Search, perform Grid Search, Problem Demonstration. Introduction to Ensemble Learning, Ensemble Learning Methods (Bagging, Boosting & Stacking), Bagging stages, Bagging Workflow, Problem Demonstration, Bagging Vs Boosting, Boosting Algorithms, Adaptive Boosting (AdaBoost), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Problem Demonstration. Introduction to Model Optimization, Elements of Optimization, Linear Programming Basics, Linear Programming Applications, Problem Demonstration, formulating Optimization Problem, Stochastic Gradient Descent (SGD), Accelerated Gradient Methods, Second-Order Methods, Problem demonstration & Case Studies.

Introduction to deep learning, use cases, structure & functionality of human brain, functionality of a machine, Neural Network, Artificial Neural Network, biological vs artificial neuron. Introduction to Perceptron, Activation Function, sigmoid function, Tanh function, Rectified Liner Unit (ReLu) function, Softmax function, Multilayer Perceptron (MLP), Neural Network Evaluation, Improving Neural Network Performance, Gradient Descent to Cost Function. Introduction to Backpropagation, Learning Rate, Neural Network Learning, Exercises. Introduction to TensorFlow, basic components, building & running a graph, Eager Execution, Introduction to Keras, TensorFlow installation, building a neural network in TensorFlow, problem demonstration, Image classification using TensorFlow. Introduction to Convolutional Neural Network (CNN), Limitations of Multilayer Perceptron, CNN vs MLP, Working of Convolutional Layer, ReLu, Pooling Layer, Fully Connected Layer, Image Recognition, Rules of Image Recognition Process, Image classification using CNN, Libraries Required for Prediction, building a CNN model, Problem Demonstration. Introduction to Recurrent Neural Network (RNN), Issues with Feed Forward Network, Architecture of RNN (One to One, Many to One, One to Many, & Many to Many), Problem Demonstration, Training RNN, Long Short-Term Memory (LSTM) networks, Issues with RNN, LSTM Structure (Forget gate, Input gate, & Output gate), Problem Demonstration. Introduction to Reinforcement Learning (RL) , use cases and challenges, RL Process, Reward Hypothesis, RL Agent Components (Environment, Agent and Information State), RL Agent Taxonomy Types, Value Based RL, Policy Based RL, & Model Based RL. Case Studies & Exercises.

Introduction to Tableau, Tableau Products, VizQL language, Data Connections, Connect to data from file, server or database, Creating Bar Charts, Line Charts & Pie Charts. Introduction to Data Grouping (group by header, group by data window, visual grouping, group hierarchies, etc.), Filtering (filtering by headers, filtering by filter cards, filtering by general tab, filtering by wildcard tab, filtering by condition tab, filtering by top tab, etc.), Problem Demonstration. Introduction to Hierarchies, creating a hierarchy, built-in hierarchies, understanding data granularity, data granularity using marks card, Sorting using toolbar, sorting using pill, sorting using marks card, sorting by legends, Problem Demonstration. Introduction to Data Blending, data blending with Tableau, Problem Demonstration, basics of Joins & Union, Inner Join, Left Outer Join, Right Outer Join, Full Outer Join, Cross Join, Joins vs Blending, Problem Demonstration. Introduction to Calculations in Tableau, types of calculations, ways to create a calculated field, Problem Demonstration, Built-In Functions (Number Function, String Function, Date Function, Logical Function, Aggregate Function, Problem Demonstration. Introduction to Table Calculations, Quick Table Calculation, Tableau Parameters, User Input Analysis, What-If Analysis, Level of Detail Calculations (LOD), LOD Parameters, Fixed LOD Expression, Include LOD Expression, Exclude LOD Expression, LOD use cases and Problem Demonstration. Introduction to Trend Lines and Reference Lines, Creating a Trend Line, Visual Grouping, p-value, R-Squared, Editing Trend Lines, Type of Trend Lines, Linear Trend, Logarithmic Trend, Exponential Trend, Polynomial Trend, Problem Demonstration. Introduction to Forecasting, Forecasting Length, Forecasting Source Data, Forecast Model, Summary Box, Problem Demonstration. Introduction to Mapping, Classification of Maps, Filled Map, Symbol Map, Density Map, Connect to a Spatial File, Interpretation of Spatial Data, Map Views from a Spatial File, Aggregate & Disaggregate Map Views, Working with Additional Data, Map Views for Analysis, Joining Spatial Files, Problem Demonstration. Introduction to Web Mapping Services (WMS), Connect to a WMS Server, WMS Background Map, Problem Demonstration, Compare Chart Items, Static Composition, Correlation, Time Comparison, Distribution, Location, KPI’s. Introduction to Dashboards in Tableau, Dashboard Approaches, Dashboard Interface, Dashboard Objects, Manipulating Objects, Web Page Object, Image Object, Building Dashboard, Problem Demonstration. Introduction to Dashboard Layouts, Containers, Tiled, Floating, Positioning & Sizing, Filtering, Dashboard Formatting, Problem Demonstration, Interactive Dashboards, Types of Actions, Filter Actions, Highlight Actions, URL Actions, Designing Dashboard for Tablets, Designing Dashboards for Mobile Phones, Problem Demonstration. Introduction to Story Points, Creating Story Point, Data Visualization Best Practices, Case Studies and Problem Demonstration.

This Data science and machine learning training program includes a range of project work and exercises to help students apply their learning to real-world problems and build portfolio. The projects and exercises are designed to give students hands-on experience with data analysis, modeling, and communication, and to build their problem-solving skills.