Data Analytics, Data Science, & Machine Learning - All in 1

What you'll learn
Understand data science foundations, applications, and the path to becoming a data scientist.
Analyze data using Python programming with variables, loops, functions, and OOP.
Apply statistics and probability with distributions, hypothesis testing, and inference in Python.
Perform data cleaning, transformation, and EDA using pandas and NumPy.
Visualize data with Python using bar charts, histograms, scatterplots, heatmaps, and box plots.
Build regression, classification, and clustering models with scikit-learn and evaluate performance.
Master advanced ML techniques like cross-validation, feature engineering, regularization, and hyperparameter tuning.
Implement ensemble learning methods such as Random Forest, AdaBoost, CatBoost, LightGBM, and XGBoost.
Explore deep learning with neural networks and TensorFlow, from preprocessing to model evaluation.
Gain hands-on experience through real-life projects and assessments to build a strong portfolio.
Acquire Excel, SQL, Python, Power BI, and ChatGPT skills to prepare for a data analyst career.
Learn data analysis foundations with statistics, hypothesis testing, and machine learning.
Use Excel for data cleaning, manipulation, formulas, functions, graphs, and charts.
Apply Excel advanced tools like pivot tables, Analysis ToolPak, and interactive dashboards.
Understand RDBMS fundamentals including keys, data types, and relational models.
Work with MySQL for table manipulation, constraints, indices, filtering, and joins.
Learn Python basics including variables, data types, lists, dictionaries, loops, and functions.
Master Python for data cleaning, manipulation, preprocessing, and transformation.
Use Python for visualization, exploratory analysis, statistics, and ML modeling.
Utilize ChatGPT for data manipulation, merging, pivot tables, and conditional logic.
Apply ChatGPT for predictive analytics with Random Forest and ML models.
Learn Power BI for data manipulation, analysis, and dashboard insights.
Create professional, story-driven dashboards in Power BI with impactful visuals.
Complete 30+ assignments, 120+ coding exercises, and 10 quizzes with 100+ questions.
Accomplish 4 capstone projects: bank churn analysis, sports analytics, HR data management and website performance analysis.
Accomplish 7 AI projects: Image Captioning, Chatbot, Voice Assistant, Text to Image, Video Summarizer, Language Translator and Data Analyst AI
Requirements
Access to computer and internet
Basic computer literacy
No coding experience required
Dedication, patience and perseverance
Description
Embark on a transformative journey into the world of Data Analytics, Data Science, and Machine Learning, where you'll learn the essential skills, tools, and mindsets to become a successful data professional. This comprehensive program is designed to take you from beginner to advanced, equipping you with the knowledge and practical experience needed to excel in the field.Whether you're looking to kickstart a career in data analytics or enhance your existing skills, this course will empower you to succeed in the dynamic world of data. Join us on this exciting path and unlock your potential in just 60-100 days of disciplined learning.Why This Course MattersMost learners struggle with fragmented resources, inconsistent guidance, or theory-heavy content that doesn't build real competence. This course solves that problem. It's structured to provide step-by-step, cumulative, and daily progress - helping you turn knowledge into capability, and capability into career readiness.We are in the AI revolution, and every industry is transforming with tools like ChatGPT, Stable Diffusion, and AI copilots for writing, coding, design, analytics, and more. This course ensures you don't just learn theory - you'll build real-world solutions that make you job-ready.1. Foundations of Data Analytics, Data Science & PythonLearn how to think like a data scientist, not just how to write code.Python fundamentals: variables, loops, conditionals, functions, data structures.Clean, modular, reusable coding practices for data workflows.Importing and handling real-world datasets with Pandas and NumPy.Data types, memory optimization, and performance tuning.A-Z data cleaning and manipulation techniques: sorting, filtering, pivot tables, and charts.2. Excel, SQL, Python & Power BI ProficiencyExcel: Manipulate data, perform calculations, and create visualizations.SQL: Query and manipulate relational databases, perform joins, aggregations, and optimize queries.Python: Analyze and visualize data with Pandas, NumPy, and Matplotlib. Automate workflows and create advanced dashboards.ChatGPT for Data Analysis: Handle missing data, outliers, dataset merging, pivoting, and even advanced ML predictions.Power BI: Connect to multiple data sources, clean and transform data, and design interactive dashboards and reports.3. Exploratory Data Analysis (EDA)Understand the shape, distributions, and essence of raw data.Advanced grouping, filtering, and reshaping with Pandas.Visualize relationships using Matplotlib and Seaborn (histograms, pairplots, heatmaps).Develop strong data intuition and hypothesis-forming skills.4. Probability, Statistics & Mathematics for Data ScienceProbability distributions: Normal, Binomial, Poisson, Exponential, Uniform.Descriptive statistics: mean, median, mode, variance, standard deviation.Inferential statistics: confidence intervals, hypothesis testing, chi-square, t-tests, ANOVA.Linear Algebra: vectors, matrices, dot products, PCA foundations.Calculus: derivatives, gradients, optimization, and gradient descent for ML.5. Machine Learning & Feature EngineeringComplete ML workflow: preprocessing, training, validating, testing.Algorithms: Logistic Regression, Decision Trees, Random Forests, KNN, Ensemble Methods.Handling class imbalance (SMOTE, stratified sampling).Model evaluation: accuracy, precision, recall, F1-score, ROC-AUC.Bias-variance tradeoff, underfitting vs. overfitting.Feature engineering: encoding categorical variables, scaling/normalizing, building pipelines.Hyperparameter tuning (GridSearchCV, RandomizedSearchCV).6. Deep Learning & Generative AINeural networks with TensorFlow: tensors, activation functions, backpropagation, optimizers.Build and train models step by step, fine-tune, and evaluate with accuracy/loss metrics.Prompt Engineering: Chain-of-Thought, Tree-of-Thought, structured prompts.Generative AI Tools & Use Cases: text, image, code, audio, and video generation.Real-world AI applications: chatbots, translators, voice assistants, text-to-image, video summarization.7. Projects & Hands-On PracticeOver 30+ assignments, 120+ coding exercises, and 10 quizzes.Capstone Projects:Bank Data AnalysisSports Data AnalysisFraud Detection & ClassificationStriker Ranking (End-to-End ML Deployment)Generative AI Projects (7 full-scale builds):Image Captioning AIChatbot with LLaMA2/GemmaAI Voice AssistantText-to-Image GeneratorAI Video SummarizerLanguage TranslatorAI Data AnalystBenefits of the CourseCareer Readiness: Gain the technical and professional skills to qualify for data analyst and data scientist roles.Versatility: Become proficient in Excel, SQL, Python, Power BI, TensorFlow, Hugging Face, and more.Problem-Solving Skills: Sharpen your analytical and critical thinking abilities.Portfolio Enhancement: Build a robust portfolio of real-world projects to showcase in interviews.Industry-Relevant Learning: Stay up-to-date with modern data and AI methodologies.How This Course Will Transform YouBy following this structured roadmap, you'll be able to:Confidently work with real datasets and perform independent analysis.Build, tune, and deploy machine learning and AI models.Understand the mathematical foundations of modern data science.Create a project portfolio strong enough for job interviews or freelance opportunities.Qualify for entry-to-intermediate level roles in Data Science, ML Engineering, or Analytics.One Honest LimitationThis course is not for learners who prefer highly animated, passive learning. The teaching style is text-based, code-first, and explanation-rich - emphasizing depth, clarity, and practical application. Diagrams and visuals are included, but the focus is on doing, thinking, and building.
Who this course is for
Everyone!
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