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

The 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 info@aclm.in

Topics Covered

PYTHON DATA ANALYTICS TRAINING OUTLINE

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

Conclusion

Who Should Attend

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

Pre-requisites

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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