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Difference between AI & ML:

    Data Science

    What about data science appeals to you ?

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      Data Science with Python 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 Python programming, how to deal with data using Python, 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 Python 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 Python programming.
      • Use data structures, function, arrays, vectors, data frames in Python.
      • Build models using statistics and Python 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 different programming languages used for Data Science.
      Python Programming 1: Basics
      • Why Python for Data Science?.
      • Installing Python.
      • Python IDEs.
      • Python Basic Syntax & Data types.
      • Lists.
      • String Manipulation.
      • Conditional Statements.
      • Looping Statements.
      • Dictionaries.
      • Tuples.
      • Functions.
      • Array.
      Python Programming 2: Libraries

      • Pandas.
      • Numpy.
      • Sci-kit Learn.
      • Matplot library.
      • Seaborn.
      Data Pre-processing & Exploration

      • Extracting data from different sources.
      • Reading XLSX, CSV etc. files.
      • Handling Missing Values.
      • Handling Outliers.
      • Different Data Munging Techniques.
      • One Hot Encoder & Feature Scaling.

      • 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.
      • Project 3.
      • Project 4.

      Download Course Curriculum


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