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Advance Certification Program in Industry

Data Science 2.0

Carrier Based Complete Data Science Training: Algorithms, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

Pre-Process & Understanding Data


  • ✓ The course provides the entire Knowledge  you need to become a data scientist
  • ✓ Fill up your resume with in demand  data science tools & skills: Statistical analysis, 
  • Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • ✓ Learn how to pre-process data Understand the mathematics behind Machine Learning
  • ✓ Start coding in Python and learn how to use it for statistical analysis

   Pro Coder
  • ✓ Perform OOPS Concepts in Python
  • ✓ Carry out cluster and factor analysis
  • ✓ Be able to understand Machine Learning algorithms in Python, 
  • using NumPy, stats models and scikit-learn ✓ Apply your skills to real-life cases & examples

   Data Science Execution
  • ✓ Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlow & Keras
  • ✓ Develop a Model while coding and solving tasks with Algorithms
  • ✓ Unfold the power of deep neural networks
  • ✓ Improve Machine Learning algorithms by studying underfitting, overfitting, training, 
  • validation, n-fold cross-validation, testing, and how hyperparameters could improve performance
  • ✓ Warm up your fingers as you will be eager to apply everything you have learned here to 
  • more and more real-life situations
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Projects

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My Skill
Hands on Projects 93%

Industry Standards

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CASE Studies 100%

Asignments

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My Skill
Algoritum Based 90%
Python Basics 
Python Data Structures
Python Fundamentals
Advance Python (oops)
Pandas  for Data Science
NumPy for Data Science
Matplotlib
Seaborn
Scikit-Learn
NLTK
Keras
Tableau – Data visualization
Tableau – Data Sources, Worksheet
Introduction power BI 
Visualize Data in the Form of Various Charts, Plots, and Maps BI tools – Power BI
Google Colab Notebook
Python – Date and Time, Data Wrangling
Python – Data Aggregation
Python – Word Tokenization, Stemming, and Lemmatization
Python – Data Visualization
Python – Statistical Analysis
Python – Types Of Distribution
Python – Correlation, Chi-Square Test, Linear Regression
Supervised Learning – Classification and Regression
Liver disease Prediction – (Logistic Regression)
Crime Analysis – (KNN Algorithm)
Classifying muffins and cupcakes – (SVM Algorithm)
Fake news detection – (Naïve Baye’s)
Android malware – (Decision tree)
Credit card Fraud detection – (Random Forest)
Evaluating the classification model – (Confusion matrix) Calculating the accuracy score.
Classification model selection For Breast Cancer – (Classification)
Employee Salary Prediction – (Linear Regression Single Variable)
Advertisement and Sales Prediction – (Multiple Linear Regression)
Generating Data points based on some equation – (Polynomial Regression)
Ice – Cream shop revenue prediction from temperature – (Decision Tree Regression)
Agriculture Price Prediction – (Random Forest Algorithm)
Evaluating the performance of my regression model – ( Root mean square error and R2 score)
Regression Model selection for sales forecasting.
Unsupervised Learning – Clustering
Crime Pattern Analysis – (K-Means Clustering)
Customer Spending Analysis – (Hierarchical Clustering)
Flower Species – Data Visualization
Image Compression Using – SVD (Singular Value Decomposition)
Unsupervised Learning – Association
Market Basket Analysis – (APRIARI)
Market Basket Optimization / Analysis – (ECLAT)
Reinforcement Learning
Web Ads click-through rate optimization – (Upper Bound Confidence) 
Natural Language Processing
Hate Speech Detection – (NLTK)
Loan Prediction Problem – (XGBoost)
Deep Learning
Movie Review Classification – (RNN)
Digits Classification – (CNN)
AI – Cart Pole (Reinforcement Learning)

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Course Syllabus

Stucture

  • > Essential Python concepts for data science, including   

  • data types, variables, loops, and functions

  • > How to work with data using Python’s powerful data manipulation libraries, 

  • such as NumPy and Pandas

  • > How to visualize data using Python libraries such as Matplotlib and Seaborn 

  • > How to visualize data using tools such as Tableau and Power bi

  • > Machine learning with Python: Supervised, Unsupervised 

  •  Reinforcement Learning 

  • > Techniques and best practices for effective data analysis and data storytelling

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