Data Science Training in Chennai
The Data Science is the combination of scientific knowledge and the statistical data. The knowledge can be acquired anywhere but the best training can be achieved only through ACTE where the training will be given by currently working professionals. Data Science Training in Chennai is provided by 'ACTE' and the designed by experts to introduce you about the fundamentals of DataScience and also to make the learning individual as an efficient DataScientist.
Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured similar to data mining. Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization.
Who is a Data Scientist?
“A Data Scientist is someone who is better at Statistics than any Software Engineer and better at Software Engineering than any Statistician.”
Data scientists use their data and analytical ability to find and interpret rich data sources; manage large amounts of data despite hardware, software, and bandwidth constraints; merge data sources; ensure consistency of datasets; create visualizations to aid in understanding data; build mathematical models using the data; and present and communicate the data insights/findings. The candidates who have undergone Data Science Course in Chennai are often expected to produce answers in days rather than months, work by exploratory analysis and rapid iteration, and to produce and present results with dashboards (displays of current values) rather than papers/reports, as statisticians normally do.
Intended Audience for Data Science Institute in Chennai:
This course is intended for:
- Security auditors and analysts
- Security professionals with little or no working knowledge of DataScience
- Systems engineers who want advanced level knowledge of and hands-on management skills on DataScience
Profiles of a Data Scientist:
- Conduct undirected research and frame open-ended industry questions
- Extract huge volumes of data from multiple internal and external sources
- Employ sophisticated analytics programs, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling
- Thoroughly clean and prune data to discard irrelevant information
- Devise data-driven solutions to the most pressing challenges
- Invent new algorithms to solve problems and build new tools to automate work
- Communicate predictions and findings to management and IT departments through effective data visualizations and reports
- Recommend cost-effective procedures to existing procedures and strategies
DataScience Training Course syllabus
- Motivating examples
- Why is it interesting?
- Several data science settings
- Introduction to the WEKA tool
- Practical information
- Interactive group discussion
- Representing problems with matrices
- Representing problem with relations
- Example: Text with TFIDF Computing simple statistics
- Means, variances, standard deviations, weighted averaging, modes, quartiles
- Example: Political predictions Simple visualizations
- Time series
- Spatial data Case studies
- X & Y examples
- Medical data
- Format of input / output data Prediction algorithms
- Decision trees
- Rule learners
- Linear/logistic regression
- Nearest neighbour learning
- Properties of prediction algorithms and practical exercises
- Combining classifiers
- Training, tuning, test data
- Holdout method, cross-validation, bootstrap method Measuring performance of a model
- Accuracy, ROC curves, precision-recall curves
- Loss functions for regression Interpretation of results
- Confidence interval for accuracy
- Hypothesis tests for comparing models, algorithms
- Filter methods
- Wrapper methods Data discretization
- Unsupervised discretization
- Supervised discretization Data transformations
- PCA and variants
- Rule of Bayes and Conditional Independence Naive Bayes
- Application to spam filtering Bayesian Networks
- Graphical representation
- Independence and correlation Temporal models
- Markov Chains
- Hidden Markov Models
- Introduction to Exploratory Data Mining Association discovery
- What is association discovery?
- What are the challenges?
- In detail: Apriori Clustering
- What is clustering?
- What are the challenges?
- In detail: agglomerative clustering
- Hands-on: clustering in WEKA 8. Case Studies in DataScience
- Eve, the Pharmaceutical Robot Scientist: Data Science for Drug Discovery
- Data science for sports analytics
- Data science for sensor data (Introduction to challenge)
1. Introduction to Data Science
What is data science, relation to data mining, machine learning, big data and statistics
2. Getting to know your dataFrom data to features
3. Overview of Tasks & Techniques: PredictionThe prediction task
4. Evaluation and Methodology of DataScienceExperimental setup
5. Data EngineeringAttribute selection
6. Overview of Tasks & Techniques: Probabilistic toolsIntroduction
7. Overview of Tasks & Techniques: Exploratory Data mining
- Free demo classes.
- Limited Batch Size.
- Excellent lab facility.
- Innovative ideas are taught by the professionals.
- 100% placement Assurance.
- Expert trainers will teach the students.
- Certificates are provided to the students.
- 5+ experienced
- Certified trainer
- Working in top MNC company
- Friendly and interactive
- Trained more than 3000 students
- Ready to help students - 24/7
- Strong knowledge and perfect delivery
- On the spot doubt clarifying person
(Marriott International Inc,.)
- Microsoft Professional Program Certificate in DataScience