Data Science

Data Science course of Anvisto is real time based with right mix of theory & practical exercises covering end to end use cases/projects

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Overview

The Data Science course is designed by keeping in mind people with minimal / No coding background and Machine Learning algorithms knowledge. It is a job oriented way of instruction with the right mix of theory & hands on, so that candidates can crack job interviews & also evolve in the Data Scientist role.

Key Highlights

One-on-One with Industry

Placement Assistance

1:1 Mock Interview

Who Can Apply?

One with logical thinking and analytical bent of mind is eligible for the course. Programming background is not mandatory. People from across the industries can benefit out the course

Curriculum

Module 1 - Introduction

Overview of course
Data Science project life cycle

Module 2 - Basics of Python - I

Installation of Python and relevant packages
Keywords, Identifiers, Comments
Indentation, Statements, Standard I/O
Variables & Data Types

Module 3 - Basics of Python - II

Operators
Control flows
Data Structures

Module 4 - Basics of Python - III

Functions, Modules, Packages
File handling
Exception handling & Debugging

Module 5 - Basics of Python - IV

Numerical Operations with NumPy
Basics of Data Frames & Operations on Data Frames
Plotting with Matplotlib

Module 6 - Basics of SQL I

Introduction to databases & SQL
USE, DESCRIBE, SHOW TABLES, SELECT
LIMIT, OFFSET, ORDER BY, DISTINCT, WHERE
Comparison operators, Logical operators

Module 7 - Basics of SQL II

Aggregate operators, GROUP BY
Joins – Left, Right, Outer
Nested Queries, DML, DDL Statements

Module 8 - Exploratory Data Analysis

Scatter plots, Pair plots
Histogram, PDF, CDF
Mean, Median, Variance, Standard Deviation
Percentiles, Quantiles, IQR
Box plots, Violin plots, Contour plots

Module 9 - Basics of Linear algebra

Vectors, Distances, Dot product
Angle between vectors, projections, unit vector
Equations of line, plane & hyperplane
Equations of circle, sphere & hypersphere
Equations of eclipse, ellipsoid & hyperellipsoid

Module 10 - Dimensionality reduction & Visualization

Standardization
Co-Variance
Principle Component of Analysis (PCA)
Eigenvalues, Eigen vectors

Project I (Classification)

Module 11 - Project Introduction

Business requirement
Problem statement

Module 12 - Data Processing

Connecting to data sources – Database / Data Warehouse / API
Data cleanup
Pre-processing data

Module 13 - Data Featurization

Featurization / Tokenization of data
Bag of Words, Bi-grams, n-grams
TF-IDF, Word2Vec
AvgW2V, TFIDF-W2V

Module 14 - Choosing ML models

Types of ML models
Choosing right ML for business problem

Module 15 - K-Nearest Neighbors (KNN)

Geometric intuition
Distance measures – Euclidean, Manhattan, Minkowski, Hamming, Cosine
Limitations, Complexities
Overfitting, Underfitting
Cross-validation

Module 16 - Various scenarios

Imbalanced data
Outliers
Standardization
Feature importance
Categorical / Numerical features
Missing values
Curse of dimensionality
Bias-Variance tradeoff

Module 17 - Evaluating models

Accuracy
Confusion matrix – Precision, Recall & F1 – score
ROC & AUC
Log-loss, R2, MAD

Module 18 - Naive Bayes

Bayes theorem, Naive Bayes
Naive Bayes on text data
Laplace smoothing
Various scenarios

Module 19 - Logistic Regression

Geometric intuition
Mathematical formulations
Regularization
Hyper parameter search
Various scenarios

Module 20 - Linear Regression

Geometric intuition
Mathematical formulations
Regularization
Various scenarios

Module 21 - Support Vector Machines (SVM)

Geometric intuition
Mathematical formulations
Kernels – Polynomial, RBF, Domain specific
Various scenarios

Module 22 - Decision Trees / Random Forest

Entropy, Information gain, Gini impurity
Constructing tree

Module 23 - Ensemble models

Bagging (Random Forest)
Boosting (GBDT)
Stacking
Cascading

Module 31 - Unsupervised Learning I

Clustering
K-means clustering

Module 32 - Unsupervised Learning II

Hierarchical clustering
DBSCAN

Module 33 - Deployment

Model persistence
Build dependencies
Move files to production system

Project II (Regression)

Miscellaneous

Module 24 - Project Introduction

Business requirement
Problem statement

Module 25 - Data Processing

Connecting to data sources
Data cleanup
Pre-processing data

Module 26 - Exploratory Data Analysis

Scatter plots, Pair plots
Histogram, PDF, CDF
Mean, Median, Variance, Standard Deviation
Percentiles, Quantiles, IQR
Box plots, Violin plots, Contour plots

Module 27 - Linear Regression

Training model
Evaluating model

Module 28 - SVM Regression

Training model
Evaluating model

Module 29 - XGBoost

Training model
Evaluating model

Module 30 - Random Forest

Training model
Evaluating model

Corporate Training

We give Corporate Employees the Training They Need to Learn & Lead

Details

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Flexible Timings

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50 Hours Training

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Certification

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24/7 Support

Student Success Stories

I recently enrolled in Anvisto’s Data Science course and it is amazing. I had no prior knowledge of data science going into the course, but I can already tell I’m going to finish with new skills. There is great support and resources available that help me understand the subject better. So far, I have nothing but positive things to say about Anvisto and its Data Science course!

Shailaja P

I took Anvisto’s data science course recently and it completely exceeded my expectations. The material is immediately applicable to my current job and I’ve already seen a huge improvement in the way I approach data analysis. I highly recommend Anvisto as a comprehensive, well-rounded data science online training for everyone!

Raj V

Anvisto’s data science course was a complete game-changer for me. It taught me everything I needed to know about working with data and running my own analysis with ease. The instructors were very knowledgeable, plus the supportive community made learning even easier. If you’re interested in getting into data science, Anvisto should be your go-to resource!

Krishna R