We are the Information Technology training division of Aspire Techsoft Pvt Ltd, an IT company founded in 2011.We are ranked among the software training institutes in the india. We are specialized in ERP, SAP, SAS, JAVA, Data Warehousing,Hadoop etc.

Kothrud Branch (Head Office)
  • Address: Malhar Building, Plot No 2, H. A. Colony,
    Paud Phata, Eardawana, Pune 38.
  • Phone:7058-198-728 / 7058-733-423
  • Email:[email protected]
Dange Chowk Branch
  • Address: Office No 108, 1st Floor, ABC Nirman Complex,
    Opp Macdonald, 16 No Stop, Near Dange Chowk, Pimpri Chinchwad, Pune - 33.
  • Phone:8856-033-664
  • Email:[email protected]

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Difference between AI & ML:

    Data Science

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      Data Science with R and Tableaue

      This course is for users who are beginners and want to learn Data Science and Machine Learning. The course is completely from scratch, If you are a student from any engineering stream, BCA, MCA, BSc, MSc or from commerce & statistical background this course will be very useful for you. To learn this there are no prerequisites, but if you have good knowledge about statistics then it will be useful for you, if not; don't worry we are going through it as well. In this course, you will learn R programming, how to deal with data using R, Statistics & Advance Statistics, Machine Learning algorithms and Data visualization with Tableau. This course will give you a kick start to your career as a Data Scientist. After this course, you also can go for advanced courses like Artificial Intelligence, Neural Network, Deep Learning etc.

      Go ahead and take this Data Science with R and Tableau course at Aspire Techsoft. Aspire Techsoft is one of the best data science training institute in Pune.

      Learn How To:

      • Read raw data from different files and data sets.
      • Manipulate data using R programming.
      • Use data structures, function, arrays, vectors, data frames in R.
      • Build models using statistics and R programming.
      • Visualize data using Table.
      • Create listing, summary, HTML, and graph reports.

      Course Outline:

      Introduction to Data Science and Analytics

      • What is Data Science ?.
      • What's the need & why it's in demand ?.
      • What is Data Analytics ?.
      • Components of Data Science.
      • Real-life examples & applications.
      • Introduction to R programming and statistics.
      R Programming 1: Basics

      • Introduction to R Programming.
      • Data Structures in R.
      • Control structure and Loops in R.
      • Vectors, creating, using, modes of vectors.
      • Arrays, creating and using.
      • Matrices 2-D array, creating, merging.
      • Factors, Lists and Data Frames.
      • R Functions.
      R Programming 2: Advance

      • Data Manipulation using Packages.
      • Graphics and Data Visualization in R.
      • Exploratory Data Analysis in R.
      • ggplot2 Package.
      • Applied Statistics using R.

      • Mean Mode, Median.
      • Random Variable.
      • Probability, Probability Distribution of Random Variables.
      • Type of Random Variables - Based on Scale of Measurement.
      • Variance & Standard Deviation.
      • Normal, Binomial, Poisson Distribution.
      • Standard Normal Distribution and Z-Score.
      • Sampling & Sampling Distribution.
      • Central Limit Theorem.
      • Simulation.
      • Hypothesis and hypothesis Testing.
      • Hypothesis Testing using z-test, t-test.
      Machine Learning

      • What is Machine Learning ?.
      • Overview & Terminologies.
      • Difference between AI & ML.
      • Supervised & Unsupervised ML.
      • Supervised ML Models.
      • Linear & Logistic Regression.
      • Regression methods, Classification.
      • Sampling & Sampling Distribution.
      • K Nearest Neighbours KNN.
      • Decision Tree, Random Forest.
      • Unsupervised ML Models.
      • K Means Clustering.
      • Under fitting and Overfitting.
      • Confusion Metrix.
      • K-Fold Cross Validation.
      • Regression Evaluation Metrics.
      • Time Series Analysis.
      • Support Vector Machine (SVM).
      • Na'ive Bayes.
      Tableau: For Data Visualization

      • Introduction to Tableau.
      • Installation, Tableau and R connectivity.
      • Calculated fields, hierarchy, parameters, sets, groups in Tableau.
      • Various visualizations Techniques in Tableau.
      • Map based visualization using Tableau.
      • Adding Totals, sub totals, Captions.
      • Formatting Options.
      • Using Combined Field, filters.
      • Table Calculations.
      • Story telling, creating dashboards.
      Project and Case Study

      • Project 1.
      • Project 2.
      • Case Study 1.
      • Case Study 2.

      Download Course Curriculum


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