DATA SCIENCE

Data Science

DATA SCIENCE : It is an inter-disciplinary field that uses scientific methods, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Power Bi is an all-in-one high-level tool for the data analytics part of data science. It can be thought of as less of a programming-language type application, but more of a high-level application akin  like Microsoft Excel. 

Duration:60 days 

key highlights

 100+ hours of learning

 Real-time industry professionals curate the course.

Internships and live projects

Dedicated staff of placement experts

Placement is guaranteed 100 percent Assistance

28+ Skills That Are Useful in the Workplace

Trainers with a minimum of 12 years of experience

Videos and back-up classes

Subject Matter Experts Deliver Guest Lectures

CONTACT US

Description of the Data Science Course

Why data science is so popular?

Data Science: In the wonderful and vast world of data science in 2020, there are a plethora of options and approaches to analytics and machine-learning. While most data scientists approach a solution using popular programming languages such as Python, R, Scala, or even Julia, there are some higher-level implementations that can get the job done in some cases. Microsoft’s Power-Bi is a great example of this.

You will need a Windows system to install Power-Bi. I’m a Fedora user, so I used Gnome’s Boxes to boot up a new virtual kernel. Boxes is a QEMU GUI that makes it incredibly simple to run multiple virtual systems under one operating system at the same time. Alternatively, you could always use the application’s online version. In my limited experience with the web-friendly version, I discovered that the features are rather lacking in comparison and are frequently split between the two.

Curriculum for the Data Science

In the wonderful and vast world of data science in 2020, there are a plethora of options and approaches to analytics and machine-learning. While most data scientists approach a solution using popular programming languages such as Python, R, Scala, or even Julia, there are some higher-level implementations that can get the job done in some cases.

Descriptive Statistics for  

Mean, Median, Mode, Quartile, Percentile, Inter-Quartile  

Range

Standard Deviation, Variance 

Descriptive Statistics for two variables 

Z-Score 

Co-variance, Co-relation 

Chi-squared Analysis 

Hypothesis Testing

 

Basic Probability, Conditional Probability 

Properties of Random Variables 

Expectations, Variance

Entropy and cross-entropy

Estimating probability of Random variable

 Understanding standard random processes

Normal Distribution 

Binomial Distribution 

Multinomial Distribution 

Bernoulli Distribution

Probability, Prior probability, Posterior probability 

Naive Bayes Algorithm

Limits,

Derivatives, Partial Derivatives 

Gradients, Significance of Gradients

How to install python (Anaconda), sciKit Learn

How to work with Jupyter Notebook and Spyder IDE 

Strings, Lists, Tuples, and Sets

Dictionaries, Control Flows, Functions

Formal/Positional/Keyword arguments

Predefined functions (range, len, enumerates etc…)

Data Frames

Packages required for data Science in R/Python

One-dimensional Array, Two-dimensional Array

Pre-defined functions (arrange, reshape, zeros, ones, empty) 

Basic Matrix operations

Scalar addition, subtraction, multiplication, division 

Matrix addition, subtraction, multiplication, division and transpose

Slicing, Indexing, Looping 

Shape Manipulation, Stacking

Series, Data Frame, Group By

 Crosstab, apply and map

 

Applications of PCA: Dimensionality Reduction 

Feature Engineering (FE)

Combine Features 

Split Features

Bar Chart 

Histogram

Box whisker

 plot Line plot

Scatter Plot and Heat Maps

Linear Regression 

Logistic Regression

Optimization (Gradient Descent etc.) 

Decision Tree

Random Forest 

Boosting and AdaBoost

Clustering Algorithms (KNN and K-Means)

 Support Vector Machines

Nave Bayes Algorithm 

Neural Networks

Text Mining (NLTK) 

Introduction to Deep learning

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