Introduction

Basics

Mathematics for DS

Data Visualization

Machine Learning

Projects

Portfolio

Internship

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Prepare yourself for a thriving career in Data Science. This course is tailored for individuals aspiring to master the essentials of data science, focusing on practical skills, industry applications, and career readiness.

Master key concepts in Data Science including data manipulation, analysis, and visualization.Develop proficiency in machine learning algorithms and their practical applications.Hands-on experience with real-world data science projects.Learn industry-relevant tools for data analysis and machine learning.Understand the application of data science in diverse industries.Explore career opportunities and continuous learning resources in Data Science.

Top Notch Faculty

Best Course Content

Real Life Projects

Job Readiness

Practical Exercises

Animated Explanation

1:1 Doubt Support

LMS Access

Students and professionals aiming for a career in Data Science.Individuals interested in mastering data manipulation, analysis, and machine learning.Anyone seeking practical experience in real-world Data Science projects.Aspiring Data Scientists looking to understand industry tools and applications.Those eager to explore career opportunities in Data Science.

**Basic Programming Knowledge:**Familiarity with the basics of programming concepts.**Mathematics Basics:**Understanding of basic mathematical concepts.

Proficiency in key Data Science concepts including data manipulation, analysis, and visualization.Mastery of machine learning algorithms and their practical applications.Hands-on experience with real-world Data Science projects.Competence in using industry-relevant tools for data analysis and machine learning.Understanding the application of Data Science in diverse industries.Prepared for placement-oriented roles in Data Science.

**This course is your gateway to a rewarding career in Data Science. Join us and embark on the journey to become a skilled Data Scientist!**

Upon completing this course, receive a recognized certificate, validating your skills and accomplishments in the field of coding and robotics.

Introduction

Basics

Mathematics for DS

Data Visualization

Machine Learning

Projects

Portfolio

Internship

Introduction to key concepts and principles of data science, including data exploration, visualization, and statistical analysis.

Learn programming languages such as Python and R for data manipulation, analysis, and building machine learning models.

Understand mathematical concepts and techniques essential for data science, including linear algebra, calculus, and probability.

Develop skills in creating compelling and informative data visualizations using tools like Matplotlib, Seaborn, and Tableau.

Explore machine learning algorithms and techniques for predictive modeling, classification, and clustering.

Apply data science skills to real-world projects, solving complex problems and showcasing practical abilities to potential employers.

Build a professional portfolio featuring completed data science projects and demonstrating expertise in various data science domains.

Prepare for data science internships by honing practical skills and gaining insights into industry practices and expectations.

- Introduction
- Using Pandas
- Pandas Datastructure
- Working with JSON and CSV Files
- Series Datastructure
- Accessing a Series Object and its Elements
- Operations on Series Object
- Series Objects vs. 1D Data Structures and 1D Numpy Arrays
- DataFrame Data Structure
- Creating and Displaying a DataFrame
- DataFrame Attributes
- Dataframe vs. Series and 2D Numpy Array
- Selecting or Accessing Data
- Adding/Modifying Rows'/Columns' Values in DataFrames
- Deleting/Renaming Columns/Rows, More on DataFrame Indexing - BOOLEAN INDEXING
- Merging and Joining DataFrames
- Iterating Over a DataFrame
- Binary Operations in a DataFrame
- Descriptive Statistics with Pandas
- Some Other Essential Functions and Functionality
- Advanced Operations on DataFrame
- Handling Missing Data
- Function groupby()

- What is data visualization
- Line Plot
- Bar Plot
- 3D Scatter-plot
- Pair plots
- Limitations of Pair plots
- Histogram and introduction to PDF (Probability Density Function)
- Univariate analysis using PDF
- CDF (Cumulative distribution function)
- Variance, Standard Deviation
- Median
- Percentiles and Quantiles
- IQR (InterQuartile Range), MAD (Median Absolute Deviation)
- Box-plot with whiskers
- Violin plots
- Heatmap
- Summarizing plots, Univariate, Bivariate, and Multivariate analysis
- Multivariate probability density, contour plot
- Projects

- Why learn it?
- Introduction to Vectors (2-D, 3-D, n-D), Row Vector and Column Vector
- Dot Product and Angle between 2 Vectors
- Projection and Unit Vector
- Matrices
- Transpose of matrix
- Inverse of matrix
- Determinant of matrix
- Trace of matrix
- Dot product
- Eigen values
- Eigen vectors
- Single value decomposition
- Equation of a line (2-D), Plane (3-D), and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane
- Distance of a point from a Plane/Hyperplane, Half-Spaces
- Equation of a Circle (2-D), Sphere (3-D), and Hypersphere (n-D)
- Equation of an Ellipse (2-D), Ellipsoid (3-D), and Hyperellipsoid (n-D)
- Square Rectangle
- Hyper Cube, Hyper Cuboid

- Introduction
- Descriptive Statistics
- Inferential Stats
- Population & Sample
- Gaussian/Normal Distribution and its PDF(Probability Density Function)
- CDF(Cumulative Density Function) of Gaussian/Normal Distribution
- Symmetric distribution, Skewness, and Kurtosis
- Standard normal variate (z) and standardization
- Kernel density estimation
- Sampling distribution & Central Limit Theorem
- Q-Q Plot: Is a given random variable Gaussian distributed?
- How distributions are used?
- Chebyshev’s inequality
- Discrete and Continuous Uniform distributions
- How to randomly sample data points. [Uniform Distribution]
- Bernoulli and Binomial distribution
- Log-normal
- Power law distribution
- Box-Cox transform
- Application of Non-Gaussian Distributions?
- Co-variance
- Pearson Correlation Coefficient
- Spearman Rank Correlation Coefficient
- Correlation vs Causation
- How to use Correlations?
- Confidence Intervals(C.I) Introduction
- Computing confidence-interval has given the underlying distribution
- C.I for the mean of a normal random variable
- Confidence Interval using bootstrapping
- Hypothesis Testing methodology, Null-hypothesis, p-value
- Hypothesis testing intuition with coin toss example
- Resampling and permutation test
- K-S Test for the similarity of two distributions
- Chi square test
- t test
- z test
- Hypothesis Testing: another example
- Resampling and permutation test: another example
- How to use Hypothesis testing?
- Proportional Sampling
- Revision Questions

- Data Cleaning: Deduplication
- Why convert text to a vector?
- Bag of Words (BoW)
- Text Preprocessing: Stemming, Stop-word removal, Tokenization, Lemmatization
- uni-gram, bi-gram, n-grams
- tf-idf (term frequency-inverse document frequency)
- Word2Vec
- Avg-Word2Vec, tf-idf weighted Word2Vec
- Bag of Words (code sample)
- Text Preprocessing (code sample)
- Bi-Grams and n-grams (code sample)
- TF-IDF (code sample)
- Word2Vec (code sample)
- Project Using NLP

- How "Classification" works?
- Classification vs Regression (examples)
- K-Nearest Neighbors Geometric intuition with a toy example
- Failure cases of K-NN
- How to measure the effectiveness of k-NN?
- Test/Evaluation time and space complexity
- k-NN Limitations
- Decision surface for K-NN as K changes
- Overfitting and Underfitting
- Need for Cross validation
- K-fold cross validation
- Visualizing train, validation and test datasets
- How to determine overfitting and underfitting?
- k-NN for regression

- Introduction to Conditional Probability
- Bayes Theorem with examples
- Naive Bayes algorithm
- Toy example: Train and test stages
- Naive Bayes on Text data
- Laplace/Additive Smoothing
- Log-probabilities for numerical stability
- Bias and Variance tradeoff
- Feature importance and interpretability
- Missing values
- Handling Numerical features (Gaussian NB)
- Multiclass classification
- Similarity or Distance matrix
- Best and worst cases
- Code example

- Geometric intuition of logistic regression
- Sigmoid function: Squashing
- Mathematical formulation of objective function
- Weight Vector
- L2 Regularization: Overfitting and Underfitting
- L1 regularization and sparsity
- Probabilistic Interpretation: Gaussian Naive Bayes
- Loss minimization interpretation
- Feature importance and model interpretability
- Collinearity of features
- Code sample
- Train & Run time space and time complexity

- Geometric Intuition of decision tree: Axis parallel hyperplanes
- Sample Decision tree
- Building a decision Tree: Entropy (Intuition behind entropy)
- Building a decision Tree: Information Gain
- Building a decision Tree: Gini Impurity
- Building a decision Tree: Constructing a DT
- Building a decision Tree: Splitting numerical features
- Feature standardization
- Categorical features with many possible values
- Overfitting and Underfitting
- Train and Run time complexity
- Regression using Decision Trees
- Cases
- Code Samples

- What is Clustering?
- Unsupervised learning
- Applications
- Metrics for Clustering
- K-Means: Geometric intuition, Centroids
- K-Means: Mathematical formulation: Objective function
- K-Means Algorithm
- How to initialize: K-Means++
- Failure cases/Limitations
- K-Medoids
- Determining the right K
- Code Samples
- Time and Space complexity
- Hierarchical clustering Technique
- DBSCAN (Density based clustering)

- Problem formulation: Movie reviews
- Content based vs Collaborative Filtering
- Similarity based Algorithms
- Matrix Factorization: PCA, SVD
- Matrix Factorization: NMF
- Matrix Factorization for Collaborative filtering
- Matrix Factorization for feature engineering
- Clustering as MF
- Hyperparameter tuning
- Cold Start problem
- Word Vectors as MF
- Eigen-Faces
- Code example

- Number System, LCM & HCF
- Percentages
- Alligations and Mixtures
- Divisibility
- Probability
- Ratio and Proportion
- Time and Work
- Time, Speed & Distance
- Geometry
- Elementary Statistics (Mean, Median, Mode, Variance, and Standard Deviation)
- Profit & Loss
- Problem on Ages
- Calender and Clocks
- Series and Progressions
- Equations
- Averages
- Area, Shapes and Perimeter
- Numbers & Decimal Fractions
- P&C
- Pie Charts
- Tabular DI
- Graphical DI
- Simplifications and calculations (Rational and Irrational Number)

- Word Pattern
- Letter Series
- Number Series
- Blood Relation
- Coding Decoding
- Data Sufficiency
- Seating Arrangement
- Directional Sense
- Statement and Conclusion
- Symbols and Notation
- Mathematical Operational Arrangement
- Syllogism
- Visual Reasoning
- Cube Folding, Paper Cuts and Folds
- Logical Venn Diagrams Based DI Questions

- HCF & LCM and Number System
- Geometry
- Ages
- Allegations and Mixtures
- Averages
- Clocks and Calendars
- Equations
- Percentages
- Permutations and Combinations
- Probability
- Profit and Loss
- Ratios and Proportion
- Series and Progressions
- Time, Speed and Distance
- Time and Work
- Mean, Median, Mode, Standard Deviation, and Variance
- Data Interpretation
- Graphical Data Interpretation
- Pie Charts
- Tabular Data Interpretation
- Simple Arithmetic Operations

Sr. Data Science Instructor at RICR

Data science trainer at RICR Ex- ML Engineer at Board Infinity