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    Python Data Analytics

    Course Duration : 90 Hours, Course Fee : 30000

    Learn Python Data Analytics with main coverage of Python Panda Module. Get the thorough insights on implementing each modules and using it in a daily routine. Register for free demo @ +91-9718812233

    Python Data AnalyticsThe Python Data Analytics training is designed to teaches engineers, data scientists, statisticians, and other quantitative professionals the Python skills they need to use with the Python programming language to analyze and creating customized chart as per the actual data.

    Candidates are suggested to opt for basic python training by ACLM to pursue this course further.

    Python Data Analytics Training and the Objective:

    • Understand the history of Python and differences between 2.X and 3.X
    • Understand the difference between Python basic data types
    • Know when to use different python collections
    • Ability to implement python functions
    • Understand control flow constructs in Python
    • Handle errors via exception handling constructs
    • Be able to quantitatively define an answerable, actionable question
    • Import both structured and unstructured data into Python
    • Parse unstructured data into structured formats
    • Understand the differences between numpy ndarrays, xlrd and pandas dataframes
    • Overview of where Python fits in the Python/Hadoop/Spark ecosystem
    • Simulate data through random number generation
    • Understand mechanisms for missing data and analytic implications
    • Explore and Clean Data
    • Create compelling graphics to reveal analytic results
    • Reshape and merge data to prepare for advanced analytics
    • Find test for group differences using inferential statistics
    • Implement linear regression from a frequentist perspective
    • Understand non-linear terms, confounding, and interaction in linear regression
    • Extend to logistic regression to model binary outcomes
    • Understand the difference between machine learning and frequentist approaches to statistics
    • Implement classification and regression models using machine learning
    • Score new datasets, evaluate model fit, and quantify variable importance

    So, What’s waiting for, just register yourself and start exploring the world by this tailor-made training on python data analytics. You can also register by dropping a mail to

    Topics Covered


     Base Python Introduction

    • History and current use
    • String Literals and numeric objects
    • Collections (lists, tuples, dicts)
    • Datetime classes in Python
    • Memory Management in Python
    • Control Flow
    • Functions
    • Exception Handling

     Defining actionable, analytic questions

    • Defining the quantitative construct to make inference on the question
    • Identifying the data needed to support the constructs
    • Identifying limitations to the data and analytic approach
    • Constructing Sensitivity analyses

     Bringing Data In

    • Structured Data
    • Working with Unstructured Text Data

     NumPy: Matrix Language

    • Introduction to the ndarray
    • NumPy operations
    • Broadcasting
    • Missing data in NumPy (masked array)
    • NumPy Structured arrays
    • Random number generation

     Data Preparation with Pandas

    • Filtering
    • Creating and deleting variables
    • Discretization of Continuous Data
    • Scaling and standardizing data
    • Identifying Duplicates
    • Dummy Coding
    • Combining Datasets
    • Transposing Data
    • Long to wide and back

     Exploratory Data Analysis with Pandas

    • Univariate Statistical Summaries and Detecting Outliers
    • Multivariate Statistical Summaries and Outlier Detection
    • Group-wise calculations using Pandas
    • Pivot Tables

     Exploring Data graphically

    • Histogram
    • Box-and-whiskers plot
    • Scatter plots
    • Forest Plots
    • Group-by plotting

     Advanced Graphing with Matplotlib, Pandas, and Seaborn

     Python, Hadoop and Spark

    • Introduction to the difference in Python, Hadoop, and Spark
    • Importing data from Spark and Hadoop to Python
    • Parallel execution leveraging Spark or Hadoop

     Missing Data

    • Exploring and understanding patterns in missing data
    • Missing at Random
    • Missing Not at Random
    • Missing Completely at Random
    • Data imputation methods

     Traditional Inferential Statistics

    • Comparing Groups
    • Correlation

     Frequentist Approaches to Multivariate Statistics

    • Linear Regression
    • Logistic regression

     Machine learning approaches to multivariate statistics

    • Machine Learning Theory
    • Data pre-processing
    • Supervised Versus Unsupervised Learning
    • Unsupervised Learning: Clustering
    • Dimensionality Reduction

     Supervised Learning: Regression

    • Linear Regression
    • Penalized Linear Regression
    • Stochastic Gradient Descent
    • Scoring New Data Sets
    • Cross Validation
    • Variance Bias-Tradeoff
    • Feature Importance

     Supervised Learning: Classification

    • Logistic Regression
    • LASSO
    • Random Forest
    • Ensemble Methods
    • Feature Importance
    • Scoring New Data Sets
    • Cross Validation


    Who Should Attend

    Python Data Analytics training is designed for data engineers, data scientists, statisticians, and other quantitative professionals.


    What You Need To Bring

    • Anaconda Python 3.5
    • Spyder IDE (Comes with Anaconda)
    • For classes delivered online, all participants need either dual monitors or a separate device logged into the online session so that they can do their work on one screen and watch the instructor on the other. A separate computer connected to a projector or large screen TV would be another way for students to see the instructor's screen simultaneously with working on their own.

    Key Takeaways

    Strong Analytics Skill in Python

    About Trainer

    • International Certifications on Python Pandas, NumPi, XLRD
    • Microsoft certified trainer
    • Certified Azure cloud network engineer
    • 12+ Years in Data Analysis, Reporting and Automation

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