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Data Science Training As the business is consistently and persistently changing process, it became mandatory for top management to take high level decisions at times so that they can launch new policies and new tradeoffs and new rules to get more market advantages than that of their competitors. But a large volume of data can be looked into and analyzed easily. That’s where Data Science will work efficiently.  By running different perspectives of business in terms of applying algorithms, statistics and R Language and Python we can analyze the data and can generate efficient reports in easy and graphical format so that top level management or steering committees can take crucial decisions over their business.

We proved, it is a SCIENCE, but not just  operating few Tools… Its not for everybody, Its for specialized people… We train you on a complete Data Architecture, Science behind the Data, Statistics, Machine Algorithms through Real-Time Use cases, Machine Learning, Python/R, Hadoop/BigData & Analytics Pipeline

Many clients of Majority of countries have identified need of data Science and machine Learning and Each client and company started implementing Data science and getting many projects but only the issue is resources in the market is very less.

As the business is consistently and persistently changing process, it became mandatory to automate [software] their complete business process to get more productivity, reducing cost, resources and infrastructure. That’s where we have thousands of Tools and technologies came into picture. But Data Science is purely a strategy in the form of science, playing behind those tools. With the increasing preponderance of Internet of Things and the field of Data Science there is increased possibility of the two fields overlapping and unifying in order to come up with new ways of solving problems and making our lives better. Most of the data that will be created in the future will be thanks to Internet of Things. Data Science will have more data to play with and due to this our world will change for good. Most of the IoT devices will be spewing data non-stop and all this data is fuel for deriving insights using the tools, techniques of Data Science. It is about converting raw data into real-time analytics. The Internet of Things will affect facets of our lives including traffic management, connected homes, medical domain, agriculture, manufacturing among other things. All this data can be deciphered in order to come up with new insights, better ways of managing and implementing things, creating new synergies, removing bottlenecks and inefficiencies and the ability to predict things in a way that can completely revolutionize the way we look at age-old problems and issues through a new shift of paradigm.

Data Science Training Institute in Hyderabad

Mr. Kaushik, MiNdLiNkS Endorsed Training Professional; Working for CMM Level 5 MNC having 8 years of IT Experience as a Senior Data Scientist.

Introduction to Data Science

• Need for Data Scientists
• Foundation of Data Science
• What is Business Intelligence / AI
• What is Data Analysis
• What is Data Mining
• What is Machine Learning
• Analytics vs Data Science
• Value Chain
• Types of Analytics
• Lifecycle Probability
• Analytics Project Lifecycle

Data

• Basis of Data Categorization
• Types of Data
• Data Collection Types
• Forms of Data & Sources
• Data Quality & Changes
• Data Quality Issues
• Data Quality Story
• What is Data Architecture
• Components of Data Architecture
• OLTP vs OLAP
• How is Data Stored?

Statistics

• Why do we need statistics for Data science?
• Types of Statistics.
• Descriptive Statistics.
  a. Measures of central tendency
  b. Measures of spread/ Dispersion
• Inferential Statistics
  a. Sampling/Population.
  b. Calculating probabilities
  c. Using discrete probability distributions
  d. Permutations and combinations
  e. Geometric, binomial, Poisson, Normal distributions
  f. Hypothesis tests – Null hypothesis, Alternative hypothesis, p-value, Chi-Square test, Anova
  g. Confidence Level
  h. Correlation and regression

R or Python language wide understanding

Machine Learning Algorithms wide understanding

• Introduction to machine learning.
• Machine Learning Process Flow
• Machine Learning Categories
• Supervised Learning
  a. Linear regression
  b. Logistic regression
  c. Decision trees
  d. Naive Bayes
  e. Support Vector Machines
  f. Random Forest
  g. Gradient Boosting Machine
  h. Neural Network

Unsupervised Learning
a. K Means.
b. Association analysis with Apriori algorithm.

Implementing the above algorithms in R/Python with use cases for an algorithm
Hadoop / BigData & Analytics Pipeline
Data Science for Internet of Things (IoT)

Complete Course:
Duration: 75 Days [Daily one hour Trainer-Led Class on all real-time scenarios where each student should carry their Laptop and Practice Assignments in front of Trainer]
Price: 40,000/- (Classroom and Online Simultaneously)

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