Dataanalysis-with-python Online Training

  • (25 REVIEWS )

Data Analysis with python is now a priority for businesses, and choosing the right data analysis tool is key to turning troves of data into usable information. However, choosing the right data analytics tool can be a challenge. First, you need to understand your business – what are the problems you’re trying to solve and which data is available to analyze? Can your team code, or will you need to use integrations? You’ll also need to check if tools are in line with your data security and governance policies. And, finally, you’ll need to consider costs

Description

Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis. ... Types of Data Analysis: Techniques and Methods.

Did you know?

Once the data is collected, cleaned, and processed, it is ready for Analysis. As you manipulate data, you may find you have the exact information you need, or you might need to collect more data.
Python is free to use for all commercial products, due to its OSI-approved open-source license.
Python has evolved as the most preferred language for Data Analytics in the current IT marketplace and analysing the current and recent search trends on Python indicate that Python is the next major innovation and is a must for Data Analytics Professionals.

Why learn and get certified in Python?

Data analysis is the process of collecting, modeling, and analyzing data to extract insights that support decision-making. There are several methods and techniques to perform analysis depending on the industry and the aim of the analysis. All these various methods for data analysis are largely based on two core areas: quantitative methods and qualitative methods in research. To explain the key differences between qualitative and quantitative data, here’s a video for your viewing pleasure:

Course Objective

After the completion of this course, Trainee will:
Master the basic and advanced concepts of Python
Learn about File and Sequence Operations
Understand Python scripts on Unix/Windows, Python editors and IDEs
Learn about the significance and installation
Learn how to use and create functions, sorting different elements, Lambda function, error and exception handling techniques and Regular expressions using modules in Python
Understand Socket programming by working on real-time projects such as FAQ Chat Application and Port Scanning Software
Learn working with MySQL database by installing MySQL-server, creating database and connecting MySQL and Python
Master Python Frameworks such as DJANGO and FLASK

Pre-requisites

For this entire analysis, I will be using a Jupyter Notebook. You can use any Python IDE you like. You will need to install libraries along the way, and I will provide links that will walk you through the installation process.

Who should attend this Training?

This certification is highly suitable for a wide range of professionals either aspiring to or are already in the IT domain, such as:

  • Anyone Who Wishes To Learn Practical Data Science Using Python
  • Anyone Interested In Learning How To Implement Machine Learning Algorithms Using Python
  • People Looking To Get Started In Deep Learning Using Python
  • People Looking To Work With Real Life Data In Python
  • Anyone With A Prior Knowledge Of Python Looking To Branch Out Into Data Analysis
  • Anyone Looking To Become Proficient In Exploratory Data Analysis, Statistical Modelling & Visualizations Using iPython

How will I perform the practical sessions in Online training?

For online training, US GlobalSoft provides the virtual environment that helps in accessing each other’s system. The detailed pdf files, reference material, course code are provided to the trainee. Online sessions can be conducted through any of the available requirements like Skype, WebEx, GoToMeeting, Webinar, etc.

DATA ANALYSIS WITH PYTHON Course Syllabus

    1. INTRODUCTION TO DAT ANALYTICS
    2. TYPES OF DATA ANALYTIVS
    3. INTRODUCTION TO PYTHON AND BASICS
    4. DATATYPES(LIST,TUPLE,SETS,DICTIONARY)
    5. FLOW CONTROLS(DECISIO N MAKING STATEMENTS,LOOPING STATEMENTS)
    6. USER DEFNED FUNCTIONS,DECORATORS
    7. FILE HANDLING PYTHON,MODULES
    8. PYTHON LIBRARIES FOR PYTHON
    9. NUMPY

Array creations, conversions, dimensional understandings, shaping, reshaping, generating sample large datasets, Linear algebra functionalities and numerical operations .

    1. 10. SCIPY LINEAR ALGEBRA OPERATIONS INTERPOLATION NUMERICAL OPERATIONS FAST FOURIER TRANSFORM
    2. 11. PANDAS:

Introduction

    1. Pandas DataFrame basics
    2. Understanding data, looking at columns, rows and cells
    3. Subsetting Columns, Rows with methods
    4. Grouped and Aggregated Calculations
    5. Frequency Means and Counts
    6. Basic plot
    7. Pandas Data Structures
    8. Creating your own data (Series and DataFrame)
    9. Series (also called as Vector) Object operations
    10. Broadcasting and Scalar operations
    11. Data Frame Broadcasting (Vectorize)
    12. Making changes to Series and DataFrame
    13. Adding additional Columns
    14. Adding additional Columns
    15. Exporting and Importing Data

12. MATPLOT LIB:

  1. Introduction
  2. Matplotlib
  3. Statistical Graphics using matplotlib
  4. Univariate
  5. Bivariate
  6. Multivariate Data
  7. Seaborn Library Plotting methodology
  8. Univariate, Bivariate and Multivariate
  9. Pandas Objects Plotting
  10. Histogram, Density Plot, Scatterplot, Hexbin Plot and Boxplot
  11. Seaborn Themes and Styles.

13. MACHINE LEARNING
Part A :Pandas and NumPy Functionalities:

    1. Linear Models
    2. Linear and Multiple Regressions using statsmodelsandsklearn
    3. Generalized Linear Models
    4. Logistic and Poisson Regressions using statsmodels and sklearn
    5. Survival Analysis
    6. Model diagnostics
    7. Residuals
    8. Comparing Multiple Models
    9. k-Fold Cross-Validation
    10. Regularization
    11. Clustering
    12. k-Means, Dimension Reduction with PCA (Principal Component Analysis)
    13. Hierarchical Clusterings
    14. Conclusions

Prepare for Certification

Our training and certification program gives you a solid understanding of the key topics covered on the Oreilly’s Dataanalysis-with-python Certification. In addition to boosting your income potential, getting certified in Dataanalysis-with-python demonstrates your knowledge of the skills necessary to be a successful Dataanalysis-with-python Developer. The certification validates your ability to produce reliable, high-quality results with increased efficiency and consistency.