# 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

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## 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 |