Become Data Science Analyst with Data Science Course in Bangalore

Become Data Science Analyst with Data Science Course in Bangalore

City: bangalore
Country: india
Phone No.: 080 - 610 12345 : 090 36363 007 : 090 35353 007
Start Date: 30-11--0001
End Date: 30-11--0001
Website URL:
Price: check online


Become Data Science Analyst with Data Science Course in Bangalore

Best of Lab facilities and Top Classroom Training of Data Science by Industry Experts at Vepsun Institute at affordable Fees. Join at BTM or Marathahalli, Centers closer to you & assistance in placement 

Why do DATA SCIENCE Course with Vepsun?


  • 1. Real time projects on Data Science will help you get hold on the Data Analysis and Data Research

  • 2. Knowledge gained from Data Science will make your skilled and secure a job in vast Data World

  • 3. Flexible timing & week end batches of Data Science course for working professinal

  • 4. Institute has trained 100’s of candidates in Data Science who are placed in good companies

Data scientists are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals.

DataScience Training:

Module 1: Introduction to Python :


  •  Concepts of Python programming
  •  Configuration of Development Environment
  •  Using the Python Interpreter
  •  Numbers and Strings


Module 2: More on Python:


  • Tuples and Lists
  •  Functions
  •  Control Flow and Loops
  •  Dictionaries


Module 3: Datascience Fundamentals :


  • Introduction to Datascience
  • Real world use-cases of Datascience
  • Walkthrough of data types
  • Datascience project lifecycle


Module 4: Introduction to NumPy:


  • Basics of NumPy Arrays
  • Mathematical operations in NumPy
  • NumPy Array manipulation
  • NumPy Array broadcasting


Module 5: Data Manipulation with Pandas :


  • Data Structures in Pandas-Series and DataFrames
  • Data cleaning in Pandas
  • Data manipulation in Pandas
  • Handling missing values in datasets
  • Hands-on: Implement NumPy arrays and Pandas DataFrames


Module 6: Data Visualization in Python :


  •  Plotting basic charts in Python
  •  Data visualization with Matplotlib
  •  Statistical data visualization with Seaborn
  •  Hands-on: Coding sessions using Matplotlib, Seaborn packages


Module 7: Exploratory Data Analysis :


  •  Introduction to Exploratory Data Analysis (EDA) steps
  •  Plots to explore relationship between two variables
  •  Histograms, Box plots to explore a single variable
  •  Heat maps, Pair plots to explore correlations
  •  Perform EDA to explore survival using titanic dataset


Module 8: Introduction to Machine Learning :


  •  What is Machine Learning?
  •  Use Cases of Machine Learning
  •  Types of Machine Learning - Supervised to Unsupervised methods
  •  Machine Learning workflow


Module 9: Linear Regression :


  •  Introduction to Linear Regression
  •  Use cases of Linear Regression
  •  How to fit a Linear Regression model?
  •  Evaluating and interpreting results from Linear Regression models
  •  Predict Bike sharing demand


Module 10: Logistic Regression :


  •  Introduction to Logistic Regression
  •  Logistic Regression use cases
  •  Understand use of odds & Logit function to perform logistic regression
  •  Predicting credit card default cases


Module 11: Decision Trees & Random Forest :


  •  Introduction to Decision Trees & Random Forest
  •  Understanding criterion(Entropy & Information Gain) used in Decision Trees
  •  Using Ensemble methods in Decision Trees
  •  Applications of Random Forest
  •  Predict passenger survival using Titanic Data set


Module 12: Model Evaluation Techniques :


  • Introduction to evaluation metrics and model selection in Machine Learning
  • Importance of Confusion matrix for predictions
  • Measures of model evaluation - Sensitivity, specificity, precision, recall & f-score
  • Use AUC-ROC curve to decide best model
  • Applying model evaluation techniques to Titanic dataset


Module 13: Dimensionality Reduction using PCA :


  •  Unsupervised Learning: Introduction to Curse of Dimensionality
  •  What is dimensionality reduction?
  •  Technique used in PCA to reduce dimensions
  •  Applications of Principle component Analysis (PCA)
  •  Optimize model performance using PCA on SPECTF heart data


Module 14: K Nearest Neighbours :


  • Introduction to KNN
  • Calculate neighbours using distance measures
  • Find optimal value of K in KNN method
  • Advantage & disadvantages of KNN
  • Classify phishing site data using close neighbour technique


Module 15: Naive Bayes Classifier :


  •  Introduction to Naive Bayes Classification
  •  Refresher on Probability theory
  •  Applications of Naive Bayes Algorithm in Machine Learning
  •  Classify spam emails based on probability


Module 16: K-means Clustering :


  • Introduction to K-means clustering
  • Decide clusters by adjusting centroids
  • Find optimal 'k value' in K-means
  • Understand applications of clustering in Machine Learning
  • Segment hands in Poker data and segment flower species in Iris flower data


Module 17: Support Vector Machines :

        Introduction to SVM

  • Figure decision boundaries using support vectors
  • Identify hyperplane in SVM
  • Applications of SVM in Machine Learning
  •  Predicting wine quality using SVM

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