Basic Data Science using Python

Overview

Job oriented Data Science certification course to learn data science and machine learning using Python! Python which once was considered as general programming language has emerged as a star of the Data Science world in recent years, owing to the flexibility it offers for end to end enterprise wide analytics implementation. This data science training covers data handling, visualization, statistical modelling and machine learning effectively with practical examples and case studies making it one of the most practical Python online training.

Duration

    40 hours

    Intended Audience

      Candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Statistics, Business Management who are not just looking for any Python course, but want Python training with advanced analytics and machine learning skills to head start their career in the field of Data science.

      Course outlines

      1.      Introduction to the data Science with Python

        • What is analytics & Data Science?
        • Common Terms in Analytics
        • Analytics vs. Data warehousing, OLAP, MIS Reporting
        • Relevance in industry and need of the hour
        • Types of problems and business objectives in various industries
        • How leading companies are harnessing the power of analytics?
        • Critical success drivers
        • Overview of analytics tools & their popularity
        • Analytics Methodology & problem solving framework
        • List of steps in Analytics projects
        • Identify the most appropriate solution design for the given problem statement
        • Project plan for Analytics project & key milestones based on effort estimates
        • Build Resource plan for analytics project
        • Why Python for data science?

        2.      Python: Essentials (Core)

          • Overview of Python- Starting with Python
          • Introduction to installation of Python
          • Introduction to Python Editors & IDE's (Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
          • Understand Jupyter notebook & Customize Settings
          • Concept of Packages/Libraries - Important packages (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
          • Installing & loading Packages & Name Spaces
          • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
          • List and Dictionary Comprehensions
          • Variable & Value Labels –  Date & Time Values
          • Basic Operations - Mathematical - string - date
          • Reading and writing data
          • Simple plotting
          • Control flow & conditional statements
          • Debugging & Code profiling
          • How to create class and modules and how to call them?

          3.      Scientific Distributions Used In Python For Data Science

            • Numpy, scify, pandas, scikitlearn, statmodels, nltk etc

            4.      Accessing/Importing And Exporting Data Using Python Modules

              • Importing Data from various sources (Csv, txt, excel, access etc)
              • Database Input (Connecting to database)
              • Viewing Data objects - subsetting, methods
              • Exporting Data to various formats
              • Important python modules: Pandas, beautifulsoup

              5.      Data Manipulation – Cleansing – Munging Using Python Modules

                • Cleansing Data with Python
                • Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
                • Data manipulation tools (Operators, Functions, Packages, control structures, Loops, arrays etc)
                • Python Built-in Functions (Text, numeric, date, utility functions)
                • Python User Defined Functions
                • Stripping out extraneous information
                • Normalizing data
                • Formatting data
                • Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

                6.      Data Analysis – Visualization Using Python

                  • Introduction exploratory data analysis
                  • Descriptive statistics, Frequency Tables and summarization
                  • Univariate Analysis (Distribution of data & Graphical Analysis)
                  • Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
                  • Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
                  • Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)
                  • Học tại Hồ Chí Minh

                  • Học tại Hà Nội

                  • Học trực tuyến


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