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This course is designed for learners interested in Data Science to take on new careers in the IT industry. Data Science is understanding data and extracting insights from it. The focus of this course is to teach you how to extract meaning from raw data so you can make better decisions and predictions. You will learn how to build predictive models, apply machine learning algorithms, mine text and images for insights, and work with large datasets.

This Data Science course will teach you some of the most important concepts like Hypothesis Testing, Basics of R Programming, Clustering, Regression Analysis, Correlation, Data Mining and more. In addition, you will gain experience managing diverse projects and tackling real-world difficulties utilising your Data Science skills through several practice-based sessions

Anyone who wants to pursue a career as a data scientist or wants to work on data science projects as part of their current job functions can enroll on this course. After completing this course, you will be able to start your career as a Data Scientist in a reputed IT firm

Data science is the process of extracting knowledge from data in order to make decisions. Data scientists are highly sought after professionals who can extract insights from data and turn them into value for their organisations. The course is easy-to-follow and will help you to get acquainted with the various aspects of Data Science. It is taught by industry and academic specialists with substantial experience in this sector. It will help you improve your new skills in real-world applications such as Data Analysis, Predictive Analytics, or creating a forecasting model.


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Course Content

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  • Why Python? Features of Python Programming, Style Installation, Print Function, Comments.
  • Variable and data types
  • Operators in python
  • Arithmetic, Assignment, Logical, Comparison, Identity , Membership
  • Collection
  • List, Tuple, Set, Dictionary
  • Conditional Statements
  • If, If-else, If-elif-else, Nested If-else
  • Looping Statements
  • for loop, while Loop, Nested loops, Range Function
  • Control Statements
  • Break , Continue, pass
  • Functions
  • Definition, Types of Function, Defining a Function, Calling a Function, Function Arguments, Lambda function
  • Scope Of Variables
  • Global, Local
  • Modules
  • Introduction, How to import?, Math module, Random Module, Packages
  • Input - Output
  • Reading Input from Keyboard, Printing Output
  • Files and Exceptions Handling

  • Introduction to Statistics
  • Introduction, Population, Sample Statistic and Parameter
  • Descriptive Statistics
  • Measures of Central Tendency Mean, Median, Mode
  • Measures of Spread Range Quartiles, Standard deviation, Variance
  • Probability
  • Terminology, Types of Probability
  • Probability Distribution, Bernoulli, Binomial, Geometric Uniform, Exponential and Normal
  • Inferential Statistics
  • Introduction, Estimation and errors, Point estimation
  • Confidence, Interval Hypothesis and its types
  • Hypothesis Testing
  • One Sample test
  • Two sample ttest
  • Anova

  • Python Packages
  • Numpy
  • Pandas
  • Scipy
  • Matplotlib
  • EDA
  • Data Manipulation
  • Data Visualization
  • Data Prepocessing : handling Null Value , categorical value Handling , feature selection and Feature scaling Etc

  • Ridge Regression
  • Lasso Regression
  • Polynomial Regression
  • SVM Regressor
  • Classification
  • Logistic Regression
  • Knearest Neighbors
  • Decision Tree Classifier
  • Naive bayes Classifier
  • SVM Classifier
  • Dimensionality Reduction Techniques
  • Introduction Method PCA, LDA
  • Clustering
  • Introduction, Kmean Clustering, Hierarchical Clustering
  • Model Selection and Hyperparameter Tuning
  • Hyper-parameters vs Parameters
  • Cross validation techniques kfold, LOOCV
  • Bootstrap, Grid Search, CV Random, Search CV
  • Ensemble Learning with case study
  • Introduction, Bagging, random forest
  • Boosting , AdaBoost ,XGBoost
  • Associate Rules Mining and Recommendations with case study
  • Introduction Apriori Algorithm
  • Recommendation, Content Based recommendation
  • Collaborative Based recommendation
  • Time Series Forecasting with case study
  • Introduction Timeseries , Components of time series in brief
  • Handling Time Series in Pandas
  • Stationarity of a Time Series, Time series models
  • Introduction to Reinforcement Learning
  • Introduction, Applications
  • Process of Reinforcement Learning, Elements of Reinforcement Learning
  • Bandit Algorithm
  • MultiArm Bandits, Greedy Approach
  • Epsilon, Greedy Approach, Upper Confidence, Bound Selection
  • Q Learning
  • Introduction, Rewards and Episodes, Algorithm, Influence
  • Intro to Machine learning
  • Intro to types of Algorithms
  • Supervised, UnSupervised , Reinforcement Learning Applications
  • Regression
  • Linear Regression

  • What Is DL and why do we require that?
  • Components of Deep Learning
  • Forward Propagation And Backward Propagation
  • Different Activation function
  • Build Artificial Neural Network Using Keras And tensor flow
  • Problem With ANN And How To Overcome That

  • Convolution Neural network
  • Different Model Based on CNN
  • Recurrent Neural network
  • Long Short term Memory

  • Into to Natural Language Processing / Purpose
  • Application of NLP
  • Tokenization , Stemming , Lemmatization
  • POS and NER
  • BOW , TF-IDF And Word Embedding
  • Into To Spacy
  • Application of Spacy
  • How to Perform All The NLP Steps In Spacy

  • Intro to Computer Vision
  • Intro To OPENCV
  • Working With Image File
  • Working With Video File
  • Corner , Edge and grid detection using OpenCV
  • Face Recognition using the OpenCV

  • Intro to Big Data And Hadoop
  • Hadoop's Core : HDFS And MapReduce
  • Programming with Pig And Spark
  • Hive, Hbase
  • Relational and Non-Relational data Store
  • Querying Data Interactively
  • Managing And Feeding data to clusters
  • Analyzing stream of data
  • Designing Of Real-world systems

  • DA Projects
  • ML Project
  • Ai Project

Skills You will Learn

  • Hypothesis Testing
  • Basics of R Programming
  • Analysis of Variance
  • Regression Analysis
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Gender and Age Detection

Use this project to predict a person’s gender and age range. The program examines different facial features (e.g. chin, eyes, eyebrows)

Brain Tumor Detection with Data Science

Develop a deep learning model that can differentiate between healthy and diseased tissue in MRI scans. This is a significant advancement that can help doctors detect brain tumours in MRI scans.

Colour Detection

Build an application to detect colours with Data Science Project. This app aims to test the viability of using machine learning to identify colours in images.

Road Lane Line Detection

Develop a system that detects lane lines in car front-facing camera images. This data can then be used to create a lane line detection algorithm for autonomous driving cars.

Fake News Detection

Build a data science powered Fake News detection model that can help detect fake news. Stop the spread of misinformation and fake news.

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LearnVern offers the most comprehensive, high-quality data science course curriculum available today. This is possible because we have an experienced team of instructors who are experts in their fields and committed to providing content that is both up-to-date and relevant to real-world problems. Besides, we offer career guidance and job-specific training to assist you in acing interviews and landing a job.

We schedule weekly three lectures of 1.5 to 2 hours each. We also have special weekend batches for working professionals. 

Yes, You will get technical guidance from our trainers. In addition, you can also discuss your technical queries during interactive sessions or submit them on our forums if you have any technical questions.

No, you cannot join lectures in the middle of the session. We offer you the flexibility to choose batch timing before starting the courses. Once you select your batch, you will have to be regular and punctual in your classes. However, if you miss a session, we will share a recorded lecture to help you catch up with the course curriculum.

After you complete the Data Science course, based on your expertise and experience, you will be able to aim for the following job roles.

  • Data Science Analyst

  • Data Scientist

  • Data Science Professional

  • Data Engineers

  • Database Administrator

  • Machine Learning Engineer