Edureka's Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Data science is a "concept to unify statistics, data analysis and their related methods" to "understand and analyse actual phenomena" with data. Data Science Training employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science from the sub-domains of machine learning, classification, cluster analysis, data mining, databases, and visualization. The Data Science Certification Course enables you to gain knowledge of the entire life cycle of Dat... Read More »
Edureka’s Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning.
Data science is a “concept to unify statistics, data analysis and their related methods” to “understand and analyse actual phenomena” with data. Data Science Training employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science from the sub-domains of machine learning, classification, cluster analysis, data mining, databases, and visualization. The Data Science Certification Course enables you to gain knowledge of the entire life cycle of Data Science, analyse and visualise different data sets, different Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes.
Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modelling, statistics and analytics. To take complete benefit of these opportunities, you need a structured training with an updated curriculum as per current industry requirements and best practices.
Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes.
Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges.
Data Science Training will help you become a Data Science Expert. It will hone your skills by helping you to understand and analyze actual phenomena with data and provide the required hands-on experience for solving real-time industry-based projects.
During this Data Science course, you will be trained by our expert instructors to:
- Gain insight into the ‘Roles’ played by a Data Scientist
- Analyze several types of data using R
- Describe the Data Science Life Cycle
- Work with different data formats like XML, CSV, etc.
- Learn tools and techniques for Data Transformation
- Discuss Data Mining techniques and their implementation
- Analyze data using Machine Learning algorithms in R
- Explain Time Series and it’s related concepts
- Perform Text Mining and Sentimental analyses on text data
- Gain insight into Data Visualization and Optimization techniques
- Understand the concepts of Deep Learning
Project#1: Movies Collection
Industry: Entertainment Industry
Description: The goal of this Use-Case is to explore the movie dataset, given the parameters like: “duration”, “movie title”, “gross collection”, “budget”, “title year”, etc. You will explore the following:
- Know top ten movies with the highest profits.
- Know top rated movies in the list and average IMDB score.
- Plot a graphical representation to show the number of movies released each year.
- Group the movies into clusters based on the Facebook likes.
- Group the directors based on movie collection and budget.
Project #2: Real Estate Price Prediction
Industry: Business Intelligence and Analytics
Description: The goal of this Use-case is to make predictions using Real Estate market data. The dataset contains the of the price of apartments in Boston. This data contains values such as “crime rate”, “age”, “accessibility”, “population” etc. Based on this data, decide on the price of new apartments.
Project #3: Diabetes Prediction
Description: The Use-Case focuses on making predictions based on the patient’s characteristic data set, the dataset contains attributes such as “glucose level”, “blood pressure”, “age” etc. At last, the goal is to make a high accuracy machine learning model to predict, whether a patient is Diabetic or not.
Project #4: Recommendation System for Grocery Store
Industry: Food Retail Industry
Description: The Use-Case scenario is to create recommendations for customers of a grocery store based upon historical transaction data, which could recommend preferable articles.
Project #5: Twitter Analytics
Industry: Social Media Analytics
Description: This Use-Case focuses on social media analytics. The problem can be defined as Measuring, Analyzing, and Interpreting interactions and associations between people, topics and ideas. The dataset to be analyzed is captured by Live Twitter Streaming.
You have to do the following:
- Perform Sentiment analysis on the tweets obtained and visualize the conclusions.
- Compare two football clubs, based on the tweets they are receiving from their fans.
Project #6: Air Passengers Forecasting
Industry: Commercial Aviation
Description: This Use-Case is about analyzing the data and applying time series model to forecast the number of bookings an Airline firm can expect each month. The dataset we will analyze contains monthly totals of international airline passengers between 1949 to 1960.You have to make informed decisions on staffing, hospitality and pricing for tickets.
Each class has practical assignments which shall be finished before the next class and helps you to apply the concepts taught during the class.
- In-depth knowledge of Data Science Life Cycle and Machine Learning Algorithms
- Comprehensive knowledge of various tools and techniques for Data Transformation
- The capability to perform Text Mining and Sentimental analyses on text data and gain an insight into Data Visualization and Optimization techniques
- The exposure to many real-life industry-based projects which will be executed in RStudio
- Projects which are diverse in nature covering media, healthcare, social media, aviation and HR
- Rigorous involvement of an SME throughout the Data Science Training to learn industry standards and best practices
The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data.
It is best suited for:
- Developers aspiring to be a 'Data Scientist'
- Analytics Managers who are leading a team of analysts
- Business Analysts who want to understand Machine Learning (ML) Techniques
- Information Architects who want to gain expertise in Predictive Analytics
- 'R' professionals who wish to work Big Data
- Analysts wanting to understand Data Science methodologies
Type of Certification
Format of Certification
Learning Objectives : Get an introduction to Data Science in this module and see how Data Science helps to analyze large and unstructured data with different tools.
- 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
Learning Objectives : In this module, you will learn about different statistical techniques and terminologies used in data analysis.
- What is Statistical Inference?
- Terminologies of Statistics
- Measures of Centers
- Measures of Spread
- Normal Distribution
- Binary Distribution
Learning Objectives : Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format.
- Data Analysis Pipeline
- What is Data Extraction
- Types of Data
- Raw and Processed Data
- Data Wrangling
- Exploratory Data Analysis
- Visualization of Data
- Loading different types of dataset in R
- Arranging the data
- Plotting the graphs
Learning Objectives : 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.
- What is Machine Learning?
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Supervised Learning algorithm: Linear Regression and Logistic Regression
- Implementing Linear Regression model in R
- Implementing Logistic Regression model in R
Learning Objectives : In this module, you should learn the Supervised Learning Techniques and the implementation of various techniques, such as Decision Trees, Random Forest Classifier, etc.
- 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
- Implementing Decision Tree model in R
- Implementing Linear Random Forest in R
- Implementing Navies Bayes model in R
- Implementing Support Vector Machine in R
Learning Objectives : Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.
- What is Clustering & its use cases
- What is K-means Clustering?
- What is C-means Clustering?
- What is Canopy Clustering?
- What is Hierarchical Clustering?
- Implementing K-means Clustering in R
- Implementing C-means Clustering in R
- Implementing Hierarchical Clustering in R
Learning Objectives : In this module, you should learn about association rules and different types of Recommender Engines.
- What is Association Rules & its use cases?
- What is Recommendation Engine & it’s working?
- Types of Recommendations
- User-Based Recommendation
- Item-Based Recommendation
- Difference: User-Based and Item-Based Recommendation
- Recommendation use cases
- Implementing Association Rules in R
- Building a Recommendation Engine in R
Learning Objectives : Discuss Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, TF-IDF and Cosine Similarity in this Module.
- The concepts of text-mining
- Use cases
- Text Mining Algorithms
- Quantifying text
- Beyond TF-IDF
- Implementing Bag of Words approach in R
- Implementing Sentiment Analysis on Twitter Data using R
Learning Objectives : In this module, you should learn about Time Series data, different component of Time Series data, Time Series modeling - Exponential Smoothing models and ARIMA model for Time Series Forecasting.
- 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
- 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
Learning Objectives : 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.
- 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
If you have a Windows system, you should have:
- Microsoft Windows 7 or newer (32-bit and 64-bit)
- Microsoft Server 2008 R2 or newer
- Intel Pentium 4 or AMD Opteron processor or newer
- 2 GB memory
- 1.5 GB minimum free disk space
- 1366 x 768 screen resolution or higher
If you have a MAC system, you should have:
- iMac/MacBook computers 2009 or newer
- OSX 10.10 or newer
- 5 GB minimum free disk space
- 1366 x 768 screen resolution or higher