course detail

Data Science Training

Data Science Training in Chennai

Data Science Training Chennai At BITA Academy– No 1 Data Science Training Institute in Chennai. Call 956600-4616 For More Details.

Data science is the investigation of where data originates from, what it speaks to and how it tends to be transformed into an important asset in the formation of business and IT systems. Mining a lot of organized and unstructured information to distinguish examples can enable an association to get control over costs, increment efficiencies, perceive new market openings and increment the association’s upper hand.

The information science field utilizes arithmetic, insights and software engineering disciplines, and joins strategies like AI, bunch investigation, information mining and perception.

For what reason would it be a good idea for me to take in Data Science from Bita?

Bita gives the best Data Science preparing for experts hoping to ace this energizing and testing field. In this instructional class, you will find out about Data Science, strategies for information securing, venture life cycle, sending Machine Learning and factual techniques, alongside considering Apache Mahout, information change and working with recommenders. You will deal with continuous tasks and well ordered assignments that have high pertinence in the corporate world, and the educational program is planned by industry specialists. Upon the fruition of the instructional class, you can apply for the absolute best employments in top MNCs around the globe at top pay rates. Bita offers lifetime access to recordings, course materials, every minute of every day backing and course material moving up to the most recent variant at no additional expense. Thus, it is obviously a one-time venture.

Course Syllabus

Part 1:Introduction to Data Science

  • What is the need for Data Scientists
  • Data Science Foundation
  • Business Intelligence
  • Data Analysis
  • Data Mining
  • Machine Learning
  • Difference between Analytics vs. Data Science
  • Analytics and it’s types
  • Lifecycle Probability
  • More about Analytics Project Lifecycle

Part 2: Python Ecosystem for Machine learning

  • Python
  • NumPy
  • Matplotlib
  • Pandas
  • SciPy
  • scikit learn

Part 3: Statistics and Mathematics

  • Inferential statistics Descriptive statistics
  • Linear Algebra Probability Calculus

Part 4: Exploratory Data analysis

  • How to Load Machine Learning Data
  • Understand Your Data with Descriptive Statistics
  • Understand Your Data with Visualization

Part 5: Feature Engineering

Prepare Your Data for Machine Learning

  • Need For Data Pre processing
  • Data Transforms
  • Rescale Data
  • Standardize Data
  • Normalize Data
  • Binarize Data(Make Binary)

Feature Selection for Machine Learning

  • Feature Selection
  • Univariate Selection
  • Recursive Feature Elimination
  • Principal Component Analysis
  • Feature Importance

Part 6: Machine learning Algorithms

Gradient Descent for Machine Learning

  • Gradient Descent
  • Batch Gradient Descent
  • Stochastic Gradient Descent
  • Tips for Gradient Descent

Linear Algorithms

  • Linear Regression
  • Logistic Regression
  • Linear Discriminant Analysis

Non linear Algorithms

  • Decision Trees
  • Naive Bayes
  • Gaussian Naïve Bayes
  • k-Nearest Neighbors
  • Learn Vector Quantization
  • Support Vector Machine

Unsupervised Algorithms

  • Cluster Algorithms
  • PCA
  • Recommendation System
  • User based Collaborative Filter
  • Item based Collaborative Filter

Part 6: Ensemble Algorithms

Bagging and Random Forest

  • Bootstrap Method
  • Bootstrap Aggregation(Bagging)
  • Random Forest
  • Estimated Performance
  • Variable Importance
  • Prepare Data For Bagged CART

Boost and AdaBoost

  • Learn An AdaBoost Model From Data
  • How To Train One Model
  • Make Predictions with AdaBoost
  • Prepare Data For AdaBoost

Part 7: XGBOOST

Part 8: Evaluation and Performance metrics of Model

Test Machine Learning Algorithms

  • Split into Train and Test Sets
  • K-fold Cross-Validation
  • Leave One Out Cross Validation
  • Repeated Random Test Train Splits
  • What Techniques to Use When

Algorithm Evaluation Metrics

  • Classification Metrics
  • Regression Metrics

Part 9: Productizing the Model

Save and Load Machine Learning Models

  • complete Your Model with pickle
  • Tips to complete Your Model
  • Create Rest services

Part 10: Final Project.

Why do you need to learn Data Science?

Demand for Data Science is huge and the IT transformation is going that requires a software professional to be well versed in Artificial intelligence,Machine learning which will be the leading technology for future. And the Salary hike for this skill is high that one should learn this technology if they look for a good position in IT.

Placement Assistance

We give 100% placement assistance to you for your best career. We conduct mock interviews during the course period. In the Mock Interviews, We will figure out your Technical competence and the areas you need to Improve. This will increase your technical skill and interview confident level.

Free Demo Classes