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The Data Science training in Delhi at Edureka is a live, instructor-led training that covers all the basic concepts of Data Science, including data visualization. Below is a look at what the Data Science course training in Delhi includes. Introduction to data science Basic understanding of statistical concepts used in data analysis Knowledge of Unsupervised Learning and clustering types used to analyze data Introduction to Reinforcement Learning and Deep learning Learn about Time Series data, components of Time Series data, and Time Series modelling Click here to know more about Data Science training in Delhi.
The data science course in Delhi covers the concept of Data collection, extraction, cleaning, exploration, transformation, integration, and mining. It also includes hands-on learning with data science tools and use cases. With this training, learners will get in-depth knowledge of the data science life cycles, such as data analysis and visualization and machine learning algorithms. Data Science is the hottest jobs of the 21st century, according to Harvard Business Review. The methodology has been adopted by many top companies since it facilitates better data analysis and visualization. The data science certification training in Delhi costs INR 20,000 approximately, and compared to the returns it offers, this cost is well worth your time and effort. Click here to know more about the Data Science course in Delhi.
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 Certification Training is designed by industry experts to make you a Certified Data Scientist. The Data Science course offers:
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:
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:
There is no specific pre-requisite for Data Science Training. However, a basic understanding of R can be beneficial. Edureka offers you a complimentary self-paced course, i.e. "R Essentials" when you enroll in Data Science Training.
Data Science with R Certification
Edureka’s Data Scientist with proficiency in R Certificate Holders work at 1000s of companies like
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Edureka's Data Science Training includes real-time industry-based projects, which will hone your skills as per current industry standards and prepare you for the upcoming Data Scientist roles.
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:
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
b>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:
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.
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