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Embark on a transformative journey with our Advanced Python Course, designed to learn python basic to advanced.

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Course Duration

40 Hrs.

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CURRICULUM & PROJECTS

Advanced Python Programming Course

    What is Data Analytic

    Common Terms in Data Analytics

    What is data

    Classication of data

    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 Data Analytics tools & their popularity.

    Data Analytics Methodology & problem-solving framework.

    List of steps in Data Analytics projects

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    Introduction To Python

    • Installation and Working with Python
    • Understanding Python variables
    • Python basic Operators
    • Understanding the Python blocks.

    Python Keyword and Identiers

    • Python Comments, Multiline Comments.
    • Python Indentation
    • Understating the concepts of Operators
      • Arithmetic
      • Relational
      • Logical
      • Assignment
      • Membership
      • Identity
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    Introduction to Statistics

    • Categorical Data
    • Numerical Data
    • Mean
    • Median
    • Mode
    • Outliers
    • Range
    • Interquartile range
    • Correlation
    • Standard Deviation
    • Variance
    • Box plot

    Understanding Statistics

    • Descriptive Statistics
    • Sample vs Population Statistics
    • Random variables
    • Probability distribution functions
    • Expected value
    • Normal distribution
    • Gaussian distribution
    • Z-score
    • Spread and Dispersion
    • Correlation and Co-variance

    Data Pre-Processing & Data Mining

    • Data Preparation
    • Feature Engineering
    • Feature Scaling
    • Datasets
    • Dimensionality Reduction
    • Anomaly Detection
    • Parameter Estimation
    • Data and Knowledge
    • Selected Applications in Data Mining

    EDA (Exploratory Data Analysis)

    • Need for structured exploratory data
    • EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
    • Identify missing data
    • Identify outliers’ data
    • Imbalanced Data Techniques
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    Data Analysis and Visualization using Pandas.

    • Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
    • How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
    • Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
    • Exploring the Data Plotting, Correlations, and Histograms
    • Getting statistical information about the data Analysis Concepts, Handle the None Values
    • Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
    • Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
    • Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
    • Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
    • Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
    • Exporting the results to Excel Joins
    • Python | Pandas Data Frame Inner Join
    • Under sting the properties of Data Frame Left Join (Left Outer Join)
    • Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
    • Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
    • Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
    • Pandas | How to Group Data How to apply Lambda / Function on Data Frame
    • Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)

    Data Analysis and Visualization using NumPy

    • Introduction to NumPy Numerical Python
    • Importing NumPy and Its Properties
    • NumPy Arrays
    • Creating an Array from a CSV
    • Operations an Array from a CSV
    • Operations with NumPy Arrays
    • Two-Dimensional Array
    • Selecting Elements from 1-D Array
    • Selecting Elements from 2-D Array
    • Logical Operation with Arrays
    • Indexing NumPy elements using conditionals
    • NumPy’s Mean and Axis
    • NumPy’s Mode, Median and Sum Function
    • NumPy’s Sort Function and More

    Data Analysis and Visualization using MatPlotLib

    • Bar Chart using Python MatPlotLib
    • Column Chart using Python MatPlotLib
    • Pie Chart using Python MatPlotLib
    • Area Chart using Python MatPlotLib
    • Scatter Plot Chart using Python MatPlotLib
    • Play with Charts Properties Using MatPlotLib
    • Export the Chart as Image
    • Understanding plt. subplots () notation
    • Legend Alignment of Chart using MatPlotLib
    • Create Charts as Image
    • Other Useful Properties of Charts.
    • Complete Understanding of Histograms
    • Plotting Different Charts, Labels, and Labels Alignment etc.

    Introduction to Data Visualization with Seaborn

    • Introduction to Seaborn
    • Making a scatter plot with lists
    • Making a count plot with a list
    • Using Pandas with seaborn
    • Tidy vs Untidy data
    • Making a count plot with a Dataframe
    • Adding a third variable with hue
    • Hue and scattera plots
    • Hue and count plots
    • Visualizing Two Quantitative Variables
      • Introduction to relational plots and subplots
      • Creating subplots with col and row
      • Customizing scatters plots
      • Changing the size of scatter plot points
      • Changing the style of scatter plot points
      • Introduction to line plots
      • Interpreting line plots
      • Visualizing standard deviation with line plots
      • Plotting subgroups in line plots
    • Visualizing a Categorical and a Quantitative Variable
      • Current plots and bar plots
      • Count plots
      • Bar plot with percentages
      • Customizing bar plots
      • Box plots
      • Create and interpret a box plot
      • Omitting outliers
      • Adjusting the whisk
      • Point plots
      • Customizing points plots
      • Point plot with subgroups
    • Customizing Seaborn Plots
      • Changing plot style and colour
      • Changing style and palette
      • Changing the scale
      • Using a custom palette
      • Adding titles and labels Part 1
      • Face Grids vs. Axes Subplots
      • Adding a title to a face Grid object
      • Adding title and labels Part 2
      • Adding a title and axis labels
      • Rotating x-tics labels
      • Putting it all together
      • Box plot with subgroups
      • Bar plot with subgroups and subplots
      • Well done! What’s next
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40 Hrs.
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Mock Interviews

Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
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FAQ's

Advanced Python Course enhances job prospects by imparting in-depth skills, making you a competitive candidate in the job market.

Job roles post Advanced Python Training include positions such as Software Developer, Data Scientist, and Solution Architect.

Taking Advanced Python Training at Croma Campus is worthwhile, offering comprehensive skills development and industry-relevant knowledge.

Learning Advanced Python Programming from basic to advanced levels is facilitated, ensuring a gradual and accessible learning curve.

Yes, the training covers basic Python Programming topics, providing a well-rounded understanding of the language.

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