3 Days Live Training on Data Analysis using Python
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Training TypeLive Training
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CategoryPython
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Duration15 Hours
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Rating4.5/5


Course Introduction
About the Course
PART 1: Introduction, Applications and Frameworks, Get Started with programming, Variables and DataTypes, Operators and Expressions , Control Structure, Sequence Types, Dictionaries and Sets ,List Comprehensions, Functions, Local, Non Local & Global Variables, Anonymous and LambdaFunctions
PART 2: Environment Set Up , Anaconda, IPython Shell, IPython Notebooks, Pycharm, Spyder IDE, RunPython scripts, Loading packages, namespaces
PART 3: Numerical Analysis using NumPy, Introduction to NumPy, NumPy overview , Creating NumPyarrays , Doing math with arrays , Indexing and slicing , Records and dates , Downloading andparsing data files , Using Scipy
PART 4: Accessing and Preparing Data, Acquiring Data with Python, Loading from CSV files, AccessingSQL databases, Cleansing Data with Python, Stripping out extraneous information, Normalizing data, Formatting data, Debugging, Code profiling
PART 5: Data manipulation with Pandas, Pandas overview , DataFrames in pandas , Using multilevelindices, Series in pandas , Statistical analysis , Grouping, aggregating and applying, scipy.stats ,Tabular Data Analysis with Pandas , Data Munging in Python using Pandas
PART 6: Visualization Tools , Overview , Mathplotlib , Numpy , Seaborn , Input: 2D, samples, and features, statistical graphics , Data Reporting , Extract datasets for specific reports (routine and adhoc) ,Prepare reports on observed trends and patterns( Daily/weekly/monthly & quarterly , Developgraphs, reports, and presentations based on observation., Create management dashboards based on derived data collections.
PART 7: Web Scrapping & NLK , NLK, Scrapy.py, urllib , Pylib , Beautiful soup
Course Objective
Learn basics of python programming
Environment Set Up and Using Various IDE
Working on Numerical analysis and Data Manipulation Techniques
Using and Applying Data Visualization and Web Scraping API’s
Generate simple reports and dashboards
Who is the Target Audience?
Fresh graduates who completed academic studies recently and get Hands on Programming.
Those who want to stay ahead in Data Analytics, Data science and frameworks
Candidates desirous of working in IT Software and Support career.
Managers and leads to manage Python and data science projects .
For those willing to switch from legacy coding to new generation Programming
Those who are willing to migrate from Non IT to IT Jobs.
For those who are looking for career breakthrough in software Development.
Basic Knowledge:
Student can start as a fresher to Learn Python.
Basic knowledge of using computer, tools and utilities
Simple analytical and logical Thinking
Interest in learning computer programming
Basic knowledge of programming helpful
Available Batches
08 Mar 2021 | Mon - Wed (03 Day) | 10:00 AM - 03:00 PM (PDT) |
15 Mar 2021 | Mon - Wed (03 Day) | 10:00 AM - 03:00 PM (PDT) |
Pricing
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Introduction,
Applications and Frameworks,
Get Started with programming,
Variables and DataTypes,
Operators and Expressions ,
Control Structure, Sequence Types,
Dictionaries and Sets ,
List Comprehensions,
Functions, Local, Non Local & Global Variables,
Anonymous and LambdaFunctions
-
Environment Set Up ,
Anaconda,
IPython Shell,
IPython Notebooks,
Pycharm, Spyder IDE,
RunPython scripts,
Loading packages,
Namespaces
-
Numerical Analysis using NumPy,
Introduction to NumPy, NumPy overview ,
Creating NumPyarrays ,
Doing math with arrays ,
Indexing and slicing ,
Records and dates ,
Downloading andparsing data files ,
Using Scipy
-
Accessing and Preparing Data,
Acquiring Data with Python,
Loading from CSV files,
AccessingSQL databases,
Cleansing Data with Python,
Stripping out extraneous information,
Normalizing data,
Formatting data,
Debugging, Code profiling
-
Data manipulation with Pandas,
Pandas overview ,
DataFrames in pandas ,
Using multilevelindices,
Series in pandas ,
Statistical analysis ,
Grouping, aggregating and applying, scipy.stats ,
Tabular Data Analysis with Pandas ,
Data Munging in Python using Pandas
-
Visualization Tools ,
Overview , Mathplotlib , Numpy , Seaborn ,
Input: 2D, samples, and features, statistical graphics ,
Data Reporting ,
Extract datasets for specific reports (routine and adhoc) ,
Prepare reports on observed trends and patterns( Daily/weekly/monthly & quarterly ,
Developgraphs, reports, and presentations based on observation.,
Create management dashboards based on derived data collections.
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Web Scrapping & NLK , NLK,
Scrapy.py, urllib , Pylib ,
Beautiful soup