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Python Data Science Training and Placement Course

  • 24×7 Doubt support
  • Classroom Course
  • Expert Trainers
  • Assignments and Quiz
  • Live projects
  • Certificate on Completion
  • Course Duration : 1.5 month (45 Days)

About

What is Data Science?

Data Science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze huge amount of data. This analysis helps data scientists to know about such things like What happened ? Why it happened? What will happen? and What can be done with the results.

By the end of this course you will..

  • Explore the fundamental concepts of data science.
  • Learn to think through the ethics surrounding privacy, data sharing and algorithmic decision-making.
  • Understand data analysis techniques for applications handling large data.
  • Be able to derive and implement optimization algorithms for these models.
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Batch Timings

  • Weekdays : Mon, Tue, Wed, Thu, Fri (2hrs)
  • Weekends : Sat, Sun (4-5hrs)

Modes Of Teaching

Class Room Course

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Live Lectures

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Course Content

Artificial Intelligence Training
1. Intro to Data Science
1.1 What is Data Science?
1.2 What is Machine Learning?
1.3 What is Deep Learning?
1.4 What is Artificial Intelligence?
1.5 What is Data Analytics?
2. Intro to Python
2.1. Variables
2.2. Data Types
2.3. Keywords
2.4. Operators
2.5. Comments
2.6. IF Else
2.7. Loops
2.8. For Loop
2.9. While Loop
2.10. Break
2.11. Continue
2.12. Pass
2.13. Strings
2.14. Lists
2.15. Tuples
2.16. Sets
2.17. Dictionary
2.18. Dictionary Function
2.19. Built In Function
2.20. Lambda Function
2.21. Regex
2.22. Arrays, Input and output
2.23. Assignment and Quiz
3. Python Packages
3.1 NumPy
3.2 Scipy
3.3 Pandas
3.4 Pytorch
3.5 Seaborn
3.6 Scikit-Learn
3.7 Matplot lib
4. Importing Data
4.1. Reading CSV Files
4.2. Saving in python data
4.3. Loading python data objects
4.4. Writing Data to CSV Files
5. Manipulating Data
5.1. Rows and Observations
5.2. Rounding Number
5.3. Selecting Columns and Fields
5.4. Merging Data
5.5. Data Aggregations
5.6. Data Munging
6. Statistics
6.1. Central Tendency
6.1.1. Mean
6.1.2. Median
6.1.3. Mode
6.2. Probability basics
6.2.1 What is Probability?
6.2.1. Types of Probability?
6.2.2. ODDS Ratio
6.3. Standard Deviation
6.3.1. Data Deviation and data Distribution
6.3.2. Variance
6.4. Bias Variance
6.4.1. Underfitting
6.4.2. Overfitting
6.5. Distance Metrices
6.5.1. Euclidean Distance
6.5.2 Manhattan Distance
6.6. Outlier Analysis
6.6.1. Inter Quartile Range
6.6.2. Box & Wishker Plot
6.6.3. Upper Wishker
6.6.4. Lower Wishker
6.6.5. Scatter Plot
6.7. Missing Value Treatment
6.7.1. What is NA?
6.7.2. Central Imputation
6.7.3. KNN Imputation
6.7.4. Dummification
6.8. Correlation
6.8.1. Pearson Correlation
6.8.2. Positive and Negative Correlation
7. Error Metrics
7.1. Classification
7.1.1 Confusion Matrix
7.1.2 Precision
7.1.3 Recall
7.1.4 Specificity
7.1.5 F1 Score
7.2. Regression
7.2.1 MSE
7.2.2 RMSE
7.2.3 MAPE
8. Machine Learning and Supervised Learning
8.1 Linear Regression
8.1.1 Linear Equation
8.1.2 Slope
8.1.3 Intercept
8.1.4 R Square value
8.2 Logistic Regression
8.2.1 ODDS Ratio
8.2.2 Probability of success
8.2.3 Probability of Failure of bias variance Tradeoff
8.2.4 ROC curve
8.2.5 Bias Variance Tradeoff
8.3 Unsupervised Learning
8.3.1 K means Clustering
8.3.2 K means ++
8.3.3 Hierarchical Clustering
8.6 Other Machine learning Algorithm
8.6.1 K Nearest Neighbor
8.6.2 Naive Bayes Classifier
8.6.3 Decision Tree C50
8.6.4 Decision Tree CART
8.6.5 Random Forest

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Training Center

Chennai Branch : Saidapet Center Address

No. 31A, E Jones Rd, Periyapet, Saidapet Chennai, Tamil Nadu

Pin Code – 600015 (IN)

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(+91) 4442064048

(+91) 9385252523

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