Usb Coaching Institute
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Data Science

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Data Science, in simple terms, is a field of study that combines programming skills, domain expertise and basic knowledge of statistics and mathematics to extract insights from the given data. USB coaching institute teaches you as Data Scientists, exactly, how and where to find the data and with business & analytical skills which questions need answering first, contributing just few minutes of your time.

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Why Choose Us?

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Job Oriented

• Customized course as per the Industry Giants need in this generation.

• We do not just provide theoretical knowledge but also offer you a customized course that will fast-track your career through our real-time projects which IT-industry giants are hunting in millennials in this pandemic.


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Project Based

• Real-time projects related to Data Science.

• USB coaching institute offers you projects that will develop your analytical skills as well as hone your programming skills, exactly what is needed in today’s competitive market.



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Portfolio Creation

• Professional online curriculum vitae, highlighting your skills, to be seen by potential employers.

• We assist you in creating online professional profile as per your skills and according to today’s IT-giants requirement, keeping you in limelight.



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3 Months of Internship

• Internship with us to enhance your career.

• We offer you 3 months of internship letter for enhancement in your career. This will get your hands on projects dealing with real time data science and machine learning projects.


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Live-Online Training

• One-on-One training with your mentor.

• We acknowledge how difficult today’s situation is, with trillions of courses out there, we stand out by providing one-on-one training live with the mentor.



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Industry Expert Trainer

• Train with the professionals having 5+ years’ experience in the industry.

• We are offering you to train with the professionals that have seen the ‘highs and lows’ of the real tech-world and know exactly what inputs you need to have an outstanding career.


What our Students say about us

Pooja Chauhan
Student

This is the best Data Science institute in Aurangabad.Idris Sir teaches us in deapth concepts of Data Science.



Aniket Gaikwad
Student

Great institute to learn Data Science. Usb coaching institute purely focus on providing practical tranining which is most important.



Sakshi Pathak
Student

Usb coaching is the best Data Science institute. My trainer was idris sir , he was ver supportive throught the session and cleared each and every doubt




Our Happy Students


Mohsin Rizvi

Mechanical Engineer

One Of Happy Student Describing His Journey ,How USB Has Helped Him To Transform His Career From A Non IT Background Him Into Data Science and Artificial Intelligence



Our Recently Placed Students


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Syllabus

    • What is Python and how it is different from other programming? • Why companies are preferring Python as generic programming Language? • Where to use Python in Day to Day IT life? • Difference between about Python2 and Python3 • Download and Installing Python from python.org • Set up Python on Windows, Mac and Linux Machine. • Understanding different Python IDE's like IDLE, PyCharm etc. • What are the unique features of Python as a language? • Use Python on Interactive shell and Programming Env. • Write your first Python Script • Understand Variables and Predefined - Keywords in Python

    • Learn conditional statements and Loops. • What are conditional statements? • What is if, else, elif blocks and how to use indentations in blocks. • Different syntax available in if-elif-else blocks. • How to define loops in Python? • What is for and while loops? • Set up Python on Windows, Mac and Linux Machine. • How to define condition on while loop. • What is iterator object and how to use them loops. • Control the loops using break and continue. • How to iterate through the various object. Understand sequence and iterable objects.

    • What is Python Built-in Classes and Objects? • Int, Float and Complex Class and Object in Python. • What are operators and how to use the in-number objects. • Str class and Objects in Python. • How to use operators in string objects. • List class and Objects in Python. • How to use operators in List, objects, Tuple class and Object • How to use operators in Tuple objects. • Dictionary Class and Object • How to use operators in dict objects • Write your first Python Script • Different data Structures, data processing Techniques to learn

    • What are functions? • Difference between Built-in and User defined functions in Python. • How to create user defined functions using def. • What is return statement and use of return in functions • Parameterize User defined function, through named and unnamed parameters Introducing Lambda functions • Str class and Objects and its functions • List class and Objects and its functions • Tuple class and and its functions. • Dictionary Class and its functions. • Write your first Python Script • Set, frozenset class and object and its functions.

    • Handle different format types of files in Python. • Int, Float and Complex Class and Object in Python • What is file class and what are different attributes and functions available? • read, readlines, readline, write, writelines, flush, close functions in Python. • What is context manager and how to use them. • Defined context manager for file handling. • Process file data.CSV, DAT, TXT, file handling. • File pointer and seek the pointer. • Introduction to Python – Built-in Modules os module in Python and functions available in os and os.path

    • What are regular expressions? • match, compile, search and findall Function. • Matching vs searching operations • Search and Replace feature using RE • Extended Regular Expressions • Wildcard characters and work with them.

    • What is Anaconda Distribution? • Jupyter Notebook • How it is different from Python Distribution? • conda repository pip and conda to get new package • pip and conda commands • set Virtual environment using conda.

    • What is Python - Pandas framework • Creating Series and data filter/transformation • Creating Data Frames on pandas • Grouping and Sorting of dataset • read data from multiple sources. csv, xls, json etc • Data analysis with data set • Practical use cases using data analysis • mini projects

    • Integrating Anaconda with Jupyter. • What is Pandas • Creating Series • Creating Data Frames • Grouping, Sorting • Group by Operations • Merging, Joining and Concatenating DataFrame • Pandas Operations • Data Input and Output from a variety of data formats like csv, excel, db, json and html • Missing Data (Imputation) • Data analysis with data set • Practical use cases using data analysis

    • numpy performance test with Python • Introduction to numpy arrays • Introduction to numpy functions • Dealing with Flat files using numpy • Mathematical functions • Statistical function • Operations with arrays

    • What is matplotlib and its gallery. • Create different graph from dataset. • pie, bar, line, horizontal bar and different for of graphs. • read data from multiple sources. csv, xls, json and plot graphs • Data analysis with data set and plot graphs • Practical use cases using data analysis • Mini Project

    • Setting up Your GitHub Account • Configuring Your First Git Repository • Making Your First Git Commit • Pushing Your First Commit to GitHub • Git and GitHub Workflow Step-by-Step

    • SQL/RDBMS database management • SQL Queries • CRUD Operations

    • Type of Dataset: Numerical, Categorical and Ordinal • Mean, Median and Mode • Variance and Standard Deviation • Probability Density Function (PDF) and Probability Mass Function (PMF) • Percentiles and Moments • Covariance and Correlation • Conditional Probability • Bayes’ Theorem Module

    • 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 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)

    • What is Machine Learning? • Machine Learning Process • Different Categories of Machine Learning: Supervised, Unsupervised and Reinforcement • Scikit-Learn Overview • Scikit-Learn cheat-sheet

    • Conditional probability . • Independent vs Mutually exclusive events • Bayes Theorem with examples • Exercise problems on Bayes Theorem • 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 • Code example

    • What is Linear Regression • Geometric intuition of Linear Regression • Mathematical formulation • Real world Cases • Code sample for Linear Regression

    • 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 • Hyperparameter Search: Grid search and random search • Column Standardization • Feature importance and Model interpretability • Collinearity of features • Train & Run time space & time complexity • Non-linearly separable data & feature engineering • Code sample: Logistic regression, GridSearchCV, RandomSearchCV • Extensions to Logistic Regression: Generalized linear models (GLM)

    • Classification and Regression • Application, Advantages and Disadvantages • Distance Metric – Euclidean, Manhattan, Chebyshev, Minkowski • Measuring accuracy using Cross-Validation, Stratified k-fold, Confusion Matrix, Precision, Recall, F1-score.

    • Classification and Regression • Separating line, Margin and Support Vectors • Linear SVC Classification • Polynomial Kernel – Kernel Trick • Gaussian Radial Basis Function (rbf) • Grid Search to tune hyper-parameters. • Support Vector Regression • Code Example

    • CART (Classification and Regression Tree) • Advantages and Disadvantages and its applications • Decision Tree Learning algorithms – ID3, C4.5, C5.0 and CART • Gini Impurity, Entropy and Information Gain • Decision Tree Regression • Visualizing a Decision Tree using graphviz module. • Regularization using tuning hyper-parameters using GridSearch CV. • Code Example

    • Unsupervised learning • 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 Example

    • Basics of Apriori Algorithm • What is Apriori • Mathematics behind the algorithm • Intuition building • Use cases • Real time code example

    • Introduction to NLP • Brief History • Speech to Text • Story Understanding • Machine Translation • Text Summarization • Text Classification • Sentiment Analysis • Text Entailment • Real time Project on Twitter for Sentiment Analysis

    • Getting Started • What is Flask • Templates • Static Files • Request Object • Sending Form Data to Template • Deployment • FastCGI