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Data Science Course with Placement in Chennai

Courses / Data Science Training

Data Science Training in Chennai


A data scientist is responsible for collecting, analysing and interpreting large amounts of data to find out the ways to help a business improve operations and gain a competitive edge over rivals. Data Science Training at Anna nagar, Chennai is provided by 'ACTE' this course is designed by experts to be of at most use to the candidates.

The Basic responsibilities of a data scientist include gathering and analyzing data, and using different types of analytics and reporting tools to detect patterns, trends and relationships in data sets. Data scientists usually work in teams to mine big data for information that can be used to predict customer behaviour and identify business risks and opportunities. The candidates who are acquiring Data Science Course at Anna nagar, Chennai,ACTE are often expected to produce answers in days basis rather than months.

A data scientist utilizes large amounts of data to develop hypotheses. ACTE has experienced trainers who assist the students to understand all the concepts from basics to advanced level and use them efficiently on the work environment. We have flexible timing that helps assist the students to benefit the most. To get a free demo and clear all your doubts contact us.

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Pre requisites for Data Science Institute at Anna nagar, Chennai:

  • Some of the technical and non-technical skills that are critical for success in data science are, if you are a potential data scientist, you can use the information herein, to frame a successful career for yourself in data science. Or If you are a data analytics director at your organization, you can use the information to train your existing team of data scientists, to make them more productive and efficient at their work.

    • Benefits of being a Data Scientist:

    • Holds a unique place between Business and IT
    • Helps in planning the future
    • Will help one develop problem-solving skills
    • Increases your critical thinking
    • Provides a life-long learning
    • Better communication skills.

    DataScience Training Course syllabus

      1. Introduction to Data Science

        What is data science, relation to data mining, machine learning, big data and statistics
      • Motivating examples
      • Why is it interesting?
      • Several data science settings
      • Introduction to the WEKA tool
      • Practical information

        • 2. Getting to know your data

        From data to features
      • 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
      • Histograms
      • Boxplots
      • Scatterplots
      • Time series
      • Spatial data
        • Case studies
      • X & Y examples
      • Medical data

        • 3. Overview of Tasks & Techniques: Prediction

        The prediction task
      • Definition
      • Examples
      • 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

        • 4. Evaluation and Methodology of DataScience

        Experimental setup
      • 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

        • 5. Data Engineering

        Attribute selection
      • Filter methods
      • Wrapper methods
        • Data discretization
      • Unsupervised discretization
      • Supervised discretization
        • Data transformations
      • PCA and variants
      • Exercises

        • 6. Overview of Tasks & Techniques: Probabilistic tools

        Introduction
      • Probabilities
      • 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

        • 7. Overview of Tasks & Techniques: Exploratory Data mining

      • 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)

    Course Highlights

    • 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.

    About Trainer

    • 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,.)
    5+ experience

    Certifications

    • Microsoft Professional Program Certificate in DataScience
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