Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.

Data Science course helps you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest and Naive Bayes using R. You’ll learn the concepts of Statistics, Time Series, Text Mining and an introduction to Deep Learning. You’ll solve real life case studies on Media, Healthcare, Social Media, Aviation, HR.

- 32 hours of online Classroom training
- Real-life case studies
- Life time access to Learning Management System (LMS)
- Practical Assignments
- 100% Money Back Guarantee
- 24/7 customer support

- Businesses Will Need One Million Data Scientists by 2021 – KDnuggets
- Roles like chief data & chief analytics officers have emerged to ensure that analytical insights drive business strategies – Forbes
- The average salary for a Data Scientist is $113k (Glassdoor)

The training is a best fit for:

- IT professionals interested in pursuing a career in analytics
- Graduates looking to build a career in analytics and data science
- Experienced professionals who would like to harness data science in their fields
- Anyone with a genuine interest in the field of data science

**Introduction to Data Science**

**Goal – Get an introduction to Data Science in this Module and see how Data Science helps to analyse large and unstructured data with different tools.**

Objectives – At the end of this Module, you should be able to:

- Define Data Science
- Discuss the era of Data Science
- Describe the Role of a Data Scientist
- Illustrate the Life cycle of Data Science
- List the Tools used in Data Science
- State what role Big Data and Hadoop, R, Spark and Machine Learning play in Data Science

Topics:

- What is Data Science?
- What does Data Science involve?
- Era of Data Science
- Business Intelligence vs Data Science
- Life cycle of Data Science
- Tools of Data Science
- Introduction to Big Data and Hadoop
- Introduction to R
- Introduction to Spark
- Introduction to Machine Learning

**Statistical Inference**

**Goal – In this Module, you should learn about different statistical techniques and terminologies used in data analysis.**

Objectives – At the end of this Module, you should be able to:

- Define Statistical Inference
- List the Terminologies of Statistics
- Illustrate the measures of Centre and Spread
- Explain the concept of Probability
- State Probability Distributions

**Topics:**

- What is Statistical Inference?
- Terminologies of Statistics
- Measures of Centers
- Measures of Spread
- Probability
- Normal Distribution
- Binary Distribution

**Data Extraction, Wrangling and Exploration**

**Goal – Discuss the different sources available to extract data, arrange the data in structured form, analyse the data, and represent the data in a graphical format.**

Objectives – At the end of this Module, you should be able to:

- Discuss Data Acquisition techniques
- List the different types of Data
- Evaluate Input Data
- Explain the Data Wrangling techniques
- Discuss Data Exploration

**Topics:**

- Data Analysis Pipeline
- What is Data Extraction
- Types of Data
- Raw and Processed Data
- Data Wrangling
- Exploratory Data Analysis
- Visualization of Data

**Hands-On/Demo:**

- Loading different types of dataset in R
- Arranging the data
- Plotting the graphs

**Introduction to Machine Learning**

**Goal – Get an introduction to Machine Learning as part of this Module. You will discuss the various categories of Machine Learning and implement Supervised Learning Algorithms.**

Objectives – At the end of this module, you should be able to:

- Define Machine Learning
- Discuss Machine Learning Use cases
- List the categories of Machine Learning
- Illustrate Supervised Learning Algorithms

**Topics:**

- What is Machine Learning?
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Supervised Learning
- Linear Regression
- Logistic Regression

**Hands-On/Demo:**

- Implementing Linear Regression model in R
- Implementing Logistic Regression model in R

**Classification**

**Goal – In this module, you should learn the Supervised Learning Techniques and the implementation of various Techniques, for example, Decision Trees, Random Forest Classifier etc.**

Objectives – At the end of this module, you should be able to:

- Define Classification
- Explain different Types of Classifiers such as,
- Decision Tree
- Random Forest
- Naïve Bayes Classifier
- Support Vector Machine

**Topics:**

- What are Classification and its use cases?
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Perfect Decision Tree
- Confusion Matrix
- What is Random Forest?
- What is Navies Bayes?
- Support Vector Machine: Classification

**Hands-On/Demo:**

- Implementing Decision Tree model in R
- Implementing Linear Random Forest in R
- Implementing Navies Bayes model in R
- Implementing Support Vector Machine in R

**Unsupervised Learning**

**Goal – Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.**

Objectives – At the end of this module, you should be able to:

- Define Unsupervised Learning
- Discuss the following Cluster Analysis
- K – means Clustering
- C – means Clustering
- Hierarchical Clustering

**Topics:**

- What is Clustering & it’s Use Cases?
- What is K-means Clustering?
- What is C-means Clustering?
- What is Canopy Clustering?
- What is Hierarchical Clustering?

**Hands-On/Demo:**

- Implementing K-means Clustering in R
- Implementing C-means Clustering in R
- Implementing Hierarchical Clustering in R

**Recommender Engines**

**Goal – In this module, you should learn about association rules and different types of Recommender Engines.**

Objectives – At the end of this module, you should be able to:

- Define Association Rules
- Define Recommendation Engine
- Discuss types of Recommendation Engines
- Collaborative Filtering
- Content-Based Filtering
- Illustrate steps to build a Recommendation Engine

**Topics:**

- What are Association Rules & it’s use cases?
- What are Recommendation Engine & it’s working?
- Types of Recommendation Types
- User-Based Recommendation
- Item-Based Recommendation
- Difference: User-Based and Item-Based Recommendation
- Recommendation Use-case

**Hands-On/Demo:**

- Implementing Association Rules in R
- Building a Recommendation Engine in R

**Text Mining**

**Goal – Discuss Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, TF-IDF and Cosine Similarity in this Module.**

Objectives – At the end of this module, you should be able to:

- Define Text Mining
- Discuss Text Mining Algorithms
- Bag of Words Approach
- Sentiment Analysis

**Topics:**

- The concepts of text-mining
- Use cases
- Text Mining Algorithms
- Quantifying text
- TF-IDF
- Beyond TF-IDF

**Hands-On/Demo:**

- Implementing Bag of Words approach in R
- Implementing Sentiment Analysis on twitter Data using R

**Time Series**

**Goal – In this module, you should learn about Time Series data, different component of Time Series data, Time Series modelling – Exponential Smoothing models and ARIMA model for Time Series forecasting.**

Objectives – At the end of this module, you should be able to:

- Describe Time Series data
- Format your Time Series data
- List the different components of Time Series data
- Discuss different kind of Time Series scenarios
- Choose the model according to the Time series scenario
- Implement the model for forecasting
- Explain working and implementation of ARIMA model
- Illustrate the working and implementation of different ETS models
- Forecast the data using the respective model

**Topics:**

- What is Time Series data?
- Time Series variables
- Different components of Time Series data
- Visualize the data to identify Time Series Components
- Implement ARIMA model for forecasting
- Exponential smoothing models
- Identifying different time series scenario based on which different Exponential Smoothing model can be applied
- Implement respective ETS model for forecasting

**Hands-On/Demo:**

- Visualizing and formatting Time Series data
- Plotting decomposed Time Series data plot
- Applying ARIMA and ETS model for Time Series forecasting
- Forecasting for given Time period

**Deep Learning**

**Goal – Get introduced to the concepts of Reinforcement learning and Deep learning in this Module. These concepts are explained with the help of Use cases. You will get to discuss Artificial Neural Network, the building blocks for artificial neural networks, and few artificial neural network terminologies.**

Objectives – At the end of this module, you should be able to:

- Define Reinforced Learning
- Discuss Reinforced Learning Use cases
- Define Deep Learning
- Understand Artificial Neural Network
- Discuss basic Building Blocks of Artificial Neural Network
- List the important Terminologies of ANN’s

**Topics:**

- Reinforced Learning
- Reinforcement learning Process Flow
- Reinforced Learning Use cases
- Deep Learning
- Biological Neural Networks
- Understand Artificial Neural Networks
- Building an Artificial Neural Network
- How ANN works
- Important Terminologies of ANN’s

**Why Instil Learning?**

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- Get trained at the best price compared to other training providers.
- Get trained by the best trainer in the industry.
- Get accesses to course specific learning videos.

**Online participants will get the session attendance link before 2-3 prior the training start date.**

***We also conduct corporate training program on your preferred location and dates with corporate discounted fee.**

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**Tel: +1 (302) 689 - 8082**