Data Science Course Online training

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Onlinetutorials.today provides online data science training course from experienced faculty.

Here is the course syllabus:

Introduction data science:

  • What is data science
  • What is AI
  • What is machine learning
  • What is deep learning

Statistics:

  • Probability
  • Types of statistics
  • Descriptive statistics
  • Measures of central tendency
  • Measures of spread
  • Central limit theorem
  • Distribution
  • 7 Different types of distributions:
  • Normal Distribution
  • Binomial distribution
  • poisson distribution
  • Probability density functions
  • Characteristics of normal distribution
  • Sampling
  • Sampling methods
  • Inferential statistics
  • P value and Z value
  • Hypothesis testing (t-test, f-test, chi-square)
  • Analysis of variance
  • Measures of relationship:
  • Correlation
  • Regression
  • Co-variance
  • Associations
  • Odds Ratio

Introduction to R-Programming

  • R and R-studion installation
  • Data types and data structures
  • Arithmetic and logical operations
  • Conditional statements
  • Loops
  • Packages and functions in R
  • Data Frame operations
  • Getting data into R from flat files
  • Connecting to databases
  • Data Inspection and Manipulation
  • Data wrangling an data munging

EDA(Exploratory data analysis and visualization)

  • Summary statistics
  • Data distributions
  • Data transformations
  • Outlier detection and management
  • One dimensional chats
  • Charts an Graphs
  • Histogram
  • Barchart
  • Two dimensional
  • Scatter plots
  • Bar charts
  • Box plots
  • Multi-dimensional plots
  • Bubble charts and word clouds
  • Inference and Variable selection

Data Pre-processing:

  • Data types an conversions
  • Binning and Normalization
  • Min-max scaling
  • Imputation
  • Dimensionality reduction

Machine Leaning Online Training:

Introduction

  • Types learning
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Supervised learning Algorithms:

Regression:

  • Linear Regression
  • Simple liner Regression
  • Variable Selection
  • Model Development
  • Gradient descent approach
  • Regression Metrics
  • Ridge regression
  • Lasso regression
  • Elastic Net Regression

Classification:

  • Logistic Regression
  • SVM
  • Decision Trees
  • Random Forest
  • Naive Bayes
  • KNN
  • Classification Metrics
  • Confusion matrix
    • Precision
    • Recall
    • F1-score
  • cross validation
  • parameter tuning

Bagging:

  • Ensemble methods
  • Random Forest

Boosting:

  • Adaboost
  • Gradient boosting
  • Xgboost

Unsupervised Algorithms

  • Clustering Algorithms
  • K-means
  • Hierarchical Clustering
  • Dimenationality Reduction
  • SVD
  • LDA
  • PCA -Principal component analysis

Reinforcement learning

  • Q-learning

Working Text Data

  • Pre processing
  • Tokenization
  • Stemming
  • Lemmatising
  • POS Tagging
  • Count vectizer
  • Bag of words
  • TF-IDF approach
  • Sentiment analysis

Time series analysis

  • Introduction
  • Stationary and non stationary data
  • Trend, Seasonality, Randomness
  • Moving Average Method
  • Exponential smoothing
  • ARIMA

Machine learning with Python

  • Introduction to Python
  • Variables
  • Operators
  • Loops
  • Functions
  • Lists
  • Tuples
  • Dictionaries
  • List comprehensive
  • Numpy:
  • Scipy:
  • Pandas:
  • Matplotlib:
  • Scikit-learn:

Introduction to Deep learning:

  • Neural Network
  • Types of networks
  • Feed forward network and Back forward network
  • CNN
  • RNN
  • LSTM
  • Gradient boosting

Recommendation systems:

  • Matrix factorization
  • collaborative filtering
  • user based collaborative filtering
  • item based collaborative filtering

Association rules

  • Market Basket Analysis
  • Apriori

FAQ’s

Why data science ?

Data science is the sexiest job for the 21st century. More job openings with less crowded. Start leaning

Who is Data scientist ? 

More than program and more than a statistician

What is average salary for data scientist ?

$102,000 in USA

How to become a data scientist ?

you need to learn statistics and programming

Is there any free resources to practice data science online ?

Is there any data science learning sources ? 

Analyticsvidhya, Towards datascience, kaggle, youtube videos are good to learn data science. If you are registered for this course, you will learn data science in 3 months.

How to use kaggle for data science ?

I am a fresher, Can i learn data science ?

Yes

I am experienced developer. Can i change my carrier to data science ?  

Yes.

What is the course Fee ?

Rs.49999 or $705

Is it online course ? 

Yes

Do you have any classroom training ? 

No

What is Refund policy ? 

We don’t have any refund policy, First we will provide the demo class, If you are interested you can enroll for online classes.

What is duration of course ?

90 Days

What is Class Timings ?

Daily 60 mints