This 6-Month Program unlocks unlimited potential, transforming you from a beginner to a Data Scientist. Gain comprehensive training and develop real-world skills that will land you a high-paying job in the field.
Engage in learning through our on demand videos released every week plus live doubt-clearing sessions for flexible learning.
Utilize a dedicated Learning Management System for easy access to all course materials, including curated notes, workbooks, and assignments.
Reinforce every theoretical concept taught through practical, real-world coding assignment, solved problems that challenge your understanding.
Develop valuable skills by working on impactful “Data Tales” – solving real-world industry problems and presenting your insights.
Receive dedicated support from experienced mentors through one-on-one consultations, chat, and email interactions.
This curriculum covers a wide range of topics in data science, from foundational statistics to deep learning and natural language processing. Here’s a concise breakdown
| 1. Understanding Probability.mp4 |
| 2. Practical usecase of probability.mp4 |
| 3. Calculate probability of event.mp4 |
| 4. Single, multiple events and exclusivity.mp4 |
| 5. Additive Rule of Probability.mp4 |
| 6. Additive Rule with joint probability.mp4 |
| 7. Conditional Probability.mp4 |
| 8. Defining Dependent Events.mp4 |
| 9. Defining Multiplicative rule and joint Probability.mp4 |
| 10. Solving problems with Multiplicative rule.mp4 |
| 11. Introducing the Tree Diagram with types of Probability.mp4 |
| 12. Type of Probability with Cart Abandonment.mp4 |
| 13. Bayes Theorem.mp4 |
| 14. Bayes Theorem Explained with brand trust example.mp4 |
| 15. Bayes Theorem for covid virus diagnosis.mp4 |
| 16. Bayes Theorem continued – Solving the issue with diagnostic report.mp4 |
| 17. DS Terminology – Population, sample.mp4 |
| 18. Dependent versus Independent Variables.mp4 |
| 19. Types of Data – Numeric versus Categorical.mp4 |
| 20. Central tendencies – Mean, Median, Mode.mp4 |
| 21. Calculation of IQR and boxplots.mp4 |
| 22. Using IQR to make sense of data.mp4 |
| 23. Measures of Dispersion – Spread, Std Dev.mp4 |
| 24. Problems on Std Dev.mp4 |
| 25. Introducing Data Distributions.mp4 |
| 26. Probability Mass Function Defining.mp4 |
| 27. Probability Density Function.mp4 |
| 28. Concept of Cumulative Mass Function.mp4 |
| 29. Binomial Distribution Explained with real example.mp4 |
| 30. Normal Distribution Properties, Skewness.mp4 |
| 31. Normal Distribution – area computation.mp4 |
| 32. Poissons Distribution.mp4 |
| 1. Introducing Inferential Statistics.mp4 |
| 2. Central Limit Theorem.mp4 |
| 3. Exercise on Central Limit Theorem.mp4 |
| 4. Verifying CLT using python.mp4 |
| 5. Introducing Confidence Intervals.mp4 |
| 6. Confidence Intervals – Explained.mp4 |
| 7. Confidence Intervals with a real Problem.mp4 |
| 8. Solving Confidence Intervals with Python Scipy.mp4 |
| 9. Confidence Intervals using Proportions.mp4 |
| 10. Shortcuts for Confidence Interval Calculation.mp4 |
| 11. Introducing Hypothesis Testing.mp4 |
| 12. Defining the Hypothesis Testing on example.mp4 |
| 13. P value In hypothesis Testing.mp4 |
| 14. Verfiying Hypothesis Testing.mp4 |
| 15. Errors in Hypothesis Testing.mp4 |
| 16. Errors in Hypothesis Testing continued.mp4 |
| 17. Problems on Hypothesis testing.mp4 |
| 18. Problems Continued on Hypothesis testing.mp4 |
| 19. Introducing T Distribution.mp4 |
| 20. T distribution for confidence Intervals.mp4 |
| 21. T Distribution for Hypothesis Testing.mp4 |
| 22. Chisquare Distribution – Defining.mp4 |
| 23. Chisquare Goodness of fit.mp4 |
| 24. Chisquare Dependency of Variable.mp4 |
| 1. Data Types in Python.mp4 |
| 1.2 Casting Datatypes in Python.mp4 |
| 2. Initialising lists.mp4 |
| 2.1 List Indexing and Slicing.mp4 |
| 2.2 List methods – append.mp4 |
| 2.3 List methods – remove elements.mp4 |
| 3. Intialising dictionary.mp4 |
| 3.1 Keys and values in Dicts.mp4 |
| 3.2 Add and remove in Dicts.mp4 |
| 4. Initialising Strings.mp4 |
| 4.1 String escape Sequences.mp4 |
| 4.2 String Operations.mp4 |
| 4.3 String indexing and slicing.mp4 |
| 4.4 String methods.mp4 |
| 4.5 String methods cont.mp4 |
| 4.6 String formatting.mp4 |
| 5. Loops In Python – if else.mp4 |
| 5.1 Loops in Python – For loop.mp4 |
| 6. Exception handling in python.mp4 |
| 7. Python Functions.mp4 |
| 7.1 Python Lambda function.mp4 |
| 1.0 Introducing Numpy array.mp4 |
| 1.1 Creating Numpy array.mp4 |
| 1.2 Creating 2d numpy array.mp4 |
| 2 More ways of creating arrays.mp4 |
| 3.1 Accessing array elements.mp4 |
| 3.2 Slicing numpy array.mp4 |
| 3.3 Negative index in numpy array.mp4 |
| 3.4 Traversing array in reverse direction.mp4 |
| 4.1 Copying a numpy array.mp4 |
| 4.2 Reshape numpy array.mp4 |
| 4.3 Transposing numpy array.mp4 |
| 4.4 Rounding numbers in numpy.mp4 |
| 5.1 Slicing array based on condition.mp4 |
| 5.2 Cumulative Sum of Np array.mp4 |
| 5.3 Common elements in numpy arrays.mp4 |
| 6.1 Random Number Generation.mp4 |
| 6.2 The Concept of Seed in Random.mp4 |
| 6.3 Generating Random Numbers.mp4 |
| 7 Broadcasting for Numpy Arrays.mp4 |
| 8.1 Any and all in Numpy.mp4 |
| 0.1 Introduction to Pandas.mp4 |
| 1.0 Initialising Dataframe.mp4 |
| 1.1 Loading a dataframe from csv.mp4 |
| 1.2 DataFrame attributes and properties.mp4 |
| 1.3 Descriptive Stats for Dataframe.mp4 |
| 1.4 Finding Missing Values in dataset.mp4 |
| 2.1 Operations – Accessing Dataset column.mp4 |
| 2.2 DF Operations – Accessing multiple columns in a list.mp4 |
| 2.3 DF operations – Indexing, Resetting index.mp4 |
| 2.4 DF operations – Slicing with Iloc.mp4 |
| 2.5 DF Operations – Slicing with Loc.mp4 |
| 2.6 DF operations – Filtering based on condition.mp4 |
| 2.7 DF operations – Value Counts using DF.mp4 |
| 2.8 DF operations – Sort Data Frame.mp4 |
| 3.1 Data Munging – Groupby.mp4 |
| 3.2 Data Munging – Binning.mp4 |
| 3.3 Data Munging – Concat Dataframes.mp4 |
| 3.4 Data Munging – Merging Data Frames.mp4 |
| 3.5 Data Munging – Crosstab.mp4 |
| 4.1 Data time in pandas.mp4 |
| 3.0 Our first Basic Line Plot.mp4 |
| 3.1 Line Plot with X, y, title.mp4 |
| 3.2 Line Plot with Line styles, markers.mp4 |
| 3.3 Line Plot adding Multiple Lines.mp4 |
| 3.4 Line Plot – Changing Xlim Ylim.mp4 |
| 3.5 Line Plot – Annotate.mp4 |
| 3.6 Line Plot – Real data.mp4 |
| 3.7 Line Plot – Multiple Lines.mp4 |
| 4.1 Scatter Plot – Basic.mp4 |
| 4.2 Scatter Plot – Point color.mp4 |
| 4.3 Scatter plot – Colors using c.mp4 |
| 4.4 Scatter Plot – Annotating a plot.mp4 |
| 4.5 Scatter Plot – Summarised.mp4 |
| 4.6 Scatter plot – real data.mp4 |
| 4.7 Scatter Plot – Creating multiple clusters.mp4 |
| 4.8 Scatter Plot – Clustering.mp4 |
| 5.1 Histogram.mp4 |
| 5.2 Histogram – Step curve.mp4 |
| 5.3 Histogram Stacked data.mp4 |
| 5.4 Histogram – real data.mp4 |
| 5.5 Histogram – Custome bin widths.mp4 |
| 5.7 Histogram – Real data.mp4 |
| 6.1 Bar Plot – Basic plot.mp4 |
| 6.2 Bar Plot – Real data.mp4 |
| 6.3 Bar Plot – Errors, Stacked.mp4 |
| 6.4 Bar Plot – Seaborn, Errors.mp4 |
| 7.1 Box Plot – Defining basics.mp4 |
| 7.2 Box Plot- Customising Box plot.mp4 |
| 8.1 Additional Plots – Violin plot.mp4 |
| 8.2 Additional Plots – Strip plot.mp4 |
| 8.3 Additional Plots – LM plot.mp4 |
| 8.4 Pairplot.mp4 |
| 8.5 Pie plot.mp4 |
| 2.1 Standard Missing Values Identification.mp4 |
| 2.2 Non Standard Missing Values Identification Numeric Variables.mp4 |
| 2.3 Non Standard Missing Values Identification – Unexpected Missing Values.mp4 |
| 2.4 Summarising missing values..mp4 |
| 3.1 Delete rows with missing values.mp4 |
| 3.2 Impute with fixed value.mp4 |
| 3.3 Impute with forward and backward fill.mp4 |
| 3.5 Impute with Transformermixin function.mp4 |
| 4.1 Conversion of data types.mp4 |
| 5 Introducing outliers and effects of Outliers.mp4 |
| 5.1 Outlier detection approaches.mp4 |
| 5.2 Inter Quartile Range for Outlier detection.mp4 |
| 5.3 Outlier Detection using Z score method.mp4 |
| 5.4 Outlier Detection using PCA and visual inspection.mp4 |
| 6.1 Categorical Encoding – Dummies.mp4 |
| 6.3 Categorical Encoding – Sklearn onehot encoder.mp4 |
| 6.4 Label Encoding – sklearn.mp4 |
| 7. Scaling Data – Standard and Min Max scaler.mp4 |
| 1. Introducing Regression.mp4 |
| 2. Covariance Analysis.mp4 |
| 3. Issues with Covariance.mp4 |
| 4. Simple linear Regression.mp4 |
| 5. Deriving the betas – SLR.mp4 |
| 6. Assumptions in Linear Regression.mp4 |
| 7.1 Metrics – RMSE.mp4 |
| 7.2 Metric – Coefficient Analysis.mp4 |
| 7.3 Metric – R Squared.mp4 |
| 8. Simple Linear Regression – Detailed Steps.mp4 |
| 9. Multiple Linear Regression derived..mp4 |
| 10. MLR – Evaluation – Adjusted R Squared.mp4 |
| 11. Measuring High Leverage points.mp4 |
| 12. Data Transformations for Linear Regression.mp4 |
| 13. Gradient Descent way to solve Linear Regression.mp4 |
| 14. Tuning learning rate in Gradient Descent.mp4 |
| 15. Multicollinearity – Solution with VIF.mp4 |
| 16. Regularisation – Overfitting.mp4 |
| 1.1 Correlations.mp4 |
| 1.2 Correlation on real data – spearman and pearson method.mp4 |
| 1.3 Correlations with heatmap.mp4 |
| 2.1 Verifying Regression assumptions linearity.mp4 |
| 2.2 Verify Regression assumptions heterscedasticity.mp4 |
| 2.3 Differentiating Linear and Non Linear Relationship.mp4 |
| 3. Outliers and High Leverage Points.mp4 |
| 3.1 Removing outliers for regression.mp4 |
| 4. Simple Linear Regression using Diabetes data.mp4 |
| 4.1 Simple Linear Regresion – Interpreting SM OLS.mp4 |
| 5. Multiple Linear Regression – Using Sklearn Boston.mp4 |
| 5.1 Multiple Linear Regression – Metrics.mp4 |
| 6. Multi Collinearity solving with VIF.mp4 |
| 7.1 Linear Regression – Indepth Analysis – Step by Step.mp4 |
| 7.2 Linear Regression – Indepth Analysis – Correlations.mp4 |
| 7.3 Linear Regression – Indepth Analysis – Processing data.mp4 |
| 7.4 Linear Regression – Indepth Analysis – Model fitting and metrics.mp4 |
| 8.1 Regularisation – Understanding on real data.mp4 |
| 8.2 Achieving Regularisation with Ridge Regression.mp4 |
| 8.3 Achieving Regularisation with Ridge Regression.mp4 |
| 9. GridSearch for regression.mp4 |
| 10.1 Visualising regression model.mp4 |
| 10.2 Printing metrics of regression models.mp4 |
| 1. Logistic Regression.mp4 |
| 2. Log Reg Definitions Continued.mp4 |
| 3. Logistic Modelling – Mathematical Equation.mp4 |
| 4. Defining Cross Entropy Loss.mp4 |
| 5. Solving the Log Reg.mkv |
| 5. Solving the Log Reg.mp4 |
| 6. Confusion Matrix.mp4 |
| 7. Precision Recall.mp4 |
| 8. Precision Versus Recall with usecase.mp4 |
| 9. Roc AUC as a metric.mkv |
| 9. Roc AUC as a metric.mp4 |
| 1. Introducing the attrition problem.mp4 |
| 1.1 Data understanding, Diagnostics.mp4 |
| 1.2 Datatype conversion.mp4 |
| 2.1 EDA Boxplot for outliers.mp4 |
| 2.2 EDA Understanding Target Variable.mp4 |
| 2.3 EDA Bivariate Analysis on DataSet.mp4 |
| 2.4 EDA Additional Insights from Data.mp4 |
| 2.5 Chisquaare test between categorical and target.mp4 |
| 3,4 Model Building and training.mp4 |
| 5.0 Model Evaluation.mp4 |
| 5.1 ROC Area under curve for evaluation.mp4 |
| 5.2 Precision Recall Curve.mp4 |
| 1. Defining Decision Trees.mp4 |
| 2. Step wise building of Decision Tree.mp4 |
| 3. Information Gain as Metric.mp4 |
| 4. Gains Ratio as metric.mp4 |
| 5. Gini Index as purity metric.mp4 |
| 6. Chisquare purity metric, when to use dtree.mp4 |
| 7. Stopping Criteria and Overfitting in D tree.mp4 |
| 8. Cart and C5.0 for decision trees.mp4 |
| 9. Regression Trees.mp4 |
| 1. Introducing the problem and Dataset.mp4 |
| 1.2 Data Understanding.mp4 |
| 1.3 Data Cleaning.mp4 |
| 1.4 Data Cleaning – Missing Values.mp4 |
| 2.1 EDA – Univariate Analysis.mp4 |
| 2.2 EDA Count Plot.mp4 |
| 2.3.1 Eda Bivariate Analysis 1.mp4 |
| 2.3.2 EDA – Bivariate Analysis 2.mp4 |
| 3.1 Modelling Essentials.mp4 |
| 4. Model Fitting.mp4 |
| 5.1 Model Evaluation – Classification report overfitting.mp4 |
| 5.2 Maximising the recall or precision.mp4 |
| 6.1 Visualising the Decision Tree.mp4 |
| 7. Fine Tuning the Decision Tree.mp4 |
| 7.3 Fine tuning Decision Tree.mp4 |
| 8 Feature Importances using Decision Tree.mp4 |
| 9 Grid Search fine tuning.mp4 |
| 1.0 Introducing SVM.mp4 |
| 2.0 SVM Mathematical Modelling.mp4 |
| 3 Deriving maximum Margin.mp4 |
| 4 Soft Margin SVM for noisy Data.mp4 |
| 5 Transformations and Kernel Trick for Non Linear Data.mp4 |
| 6 Summary of SVM.mp4 |
| 1.1 Introducing Problem and Dataset.mp4 |
| 1.2 Exploring the Data – Describe, analyse age.mp4 |
| 2.1 Missing Values and connection with SVM.mp4 |
| 2.2 Data Cleaning – Junk values, structural errors.mp4 |
| 2.3 Imputation for Missing values using KNN DF.mp4 |
| 3. Outlier Analysis with Unvariate Analysis.mp4 |
| 4.1 Univariate Analysis – Target Column.mp4 |
| 4.2 Univariate Analysis Categorical Features.mp4 |
| 4.3 Univariate Analysis Numeric Features.mp4 |
| 5.1 Bivariate Analysis Correlation heatmap.mp4 |
| 5.2 Bivariate Analysis Numeric versus Categorical features.mp4 |
| 5.3 Bivariate Analysis categorical versus categorical column.mp4 |
| 6.1 Data Preparation for Modelling.mp4 |
| 6.2 Normalising the Data using Minmax.mp4 |
| 7 Running the models.mp4 |
| 8. Kernels and when to use them – rbf kernel.mp4 |
| 9. Finetuning Hyper parameters for SVM.mp4 |
| 1. Introducing KNN.mp4 |
| 2. Distance Metrics in KNN.mp4 |
| 3. Distance Metrics Continued.mp4 |
| 4. Variants and steps fo best KNN.mp4 |
| 1. Introduction to neural networks.mp4 |
| 10. Backward Propogation.mp4 |
| 2. The Artificial neuron.mp4 |
| 3. Perceptron Model.mp4 |
| 4. MLP solving XOR gate.mp4 |
| 5. MLP – Need for Activation Function.mp4 |
| 6. Activation Functions – In Detailed.mp4 |
| 7. Refined Versions of Relu, Bias.mp4 |
| 8. Forward Propogation.mp4 |
| 9. Forward Prop, Errors – Continued.mp4 |
| 1. Build your first AI app – chatbot or Code an online Saas in Minutes |
| 2. History of Language and Language Modelling , |
| 3. Word Representations – Embeddings / Word2Vec / Tokens |
| 4. Prompt Engineering & Text Generation |
| 5. Zero Shot and Few shot learning |
| 6. Building our own LLM : Using the transformers |
| 7. Language Modelling & Transformer Architecture – in Detailed |
| 8. Building the LLM from the scratch – Transformer Architecture |
| 9. Classify reviews or movie plots or story lines |
| 10. How to classify anything or everything using LLMs |
| 11. Fine Tuning Language Models for domain specific cases |
| 12. Real World case study using LLMs : Clustering & Topic Modelling |
| 13. Dense Retrieval, Reranking and Evaluation of Retrieval |
| 14. RAG : Grounded Generation, RAG with local Models |
| 15. Advanced RAG, Evaluation of RAG |
| 16. Types of Agents – Control,Autonomy, |
| 17. Tools in AI , Available tools, how to include in workflows |
| 18. Agentic RAG & MCP in context of Agents |
| 19. Planning & Reasoning, Memory in Context of Agents |
| 20. Multi Agentic Systems and two enterprise use cases |
This curriculum equips you with the necessary skills and knowledge to become a successful data scientist.
