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# Introduction to Data Science

### A quick search for Data Scientist on Naukri.com reveals over** 9496 open positions**

Data science job **trend is growing** and according to **Carrercast.com data scientist jobs are toughest jobs** to be filled and the **best growth potential** over the next seven years

Next Space brings you a comprehensive introductory course to kick start your Data Science journey

### Course curriculum in Brief:

- How to load data
- How to clean real world data
- How to make meaning from noisy data
- How to model data using machine learning
- How to visualize data
- Introduction to R Programming, Python and Hadoop

## Details Course Curriculum

**Overview of Data science : **

- Big Data
- Big Data applications
- Characteristics of Big Data (Volume Variety and Velocity)
- Challenges of Big data
- Data science
- Components of Data science
- Data science prospects
- Types of data scientists
- Data science process

**Data exploration and Presentation: **

- Data Visualization, Box Plot, Basic Statistics (descriptive statistics)
- Introduction to Exploratory data analysis (EDA)
- Background on Statistical methods for evaluation

** ****Probability distributions: **

- Probability definition,
- Probability rules,
- Important theorems in probability

**Hypothesis Testing: **

- Hypothesis,
- Steps in Hypothesis Testing,
- Steps in Hypothesis Testing,
- Different testing procedure.

** ****Correlation and Regression: **

- Cross tabulations,
- Chi square,
- correlation analysis,
- Linear regression model,
- regression coefficients,
- Interpreting a model

**Logistic Regression: **

- Assumptions,
- Link functions,
- Logit transformations,
- Regression Model,
- Regression coefficients and interpretation,
- ROC Curve. (This is optional)

** ****Machine learning:**

- What is machine learning
- Applications of machine learning
- Data processing /manipulation
- Background on supervised and supervised learning

## Introduction to R Programming

**Introduction to R: **

- Installation of R
- Installing Packages/Libraries
- Basic operations
- Importing data

** ****Statistics in R:**

- Descriptive statistics,
- Correlation,
- Cross tabulation and Chi square
- ANOVA

**Predictive Models in R: **

- Linear Regression,
- Logistic Regression

** ****Unsupervised Learning Models:**

- K means clustering in R

**Supervised Learning Models:**

- KNN algorithms
- Naïve Bayes theorem
- Neural Networks
- Support Vector Machines

**Introduction to Hadoop and Python:**

**Python Basics:**

- Python Installation
- Basic operations
- Libraries

**Hadoop Basics:**

- Introduction to Hadoop
- Installation
- Basic operations.

**Trainer Profile**

**Tharangini** has trained over 250 Individual and aided them in achieving excellence in their career. She has over 7 years of experience in training in analytics and consulting in analytics services. She has also worked in machine learning algorithms. And have expertise in R, SPSS and SAS