Datascience With Python Online Training

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Data Science stream ranks first among the top trending jobs on Linkedin. This gonna be the next revolution in Information Technology as it drives the world. The future is all about Data Science/Artificial Intelligence – Better decisions, better tools, and better life.

Description

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value.

Did you know?

Data Science Master class program helps in combining the disruption into categories and communicating their potential, which allows data and analytics leaders to drive better results. Top businesses thought there is a necessity to analyze the data for significant benefits. They use the insights from data for the benefit of users.

Why learn and get certified in Data Science with Python ?

This Data Science course is best for individuals who are looking to transform their careers. People who have the passion to use the data, analyze, visualize and use it for the betterment of the Business and the society. For those mathematics enthusiasts, who can apply maths in real life and solve complex business challenges. This is specifically ideal for the people who are
1)Analysts and Software engineers looking for a career shift in the data science stream.
2)Freshers who want to start the career as we teach from the basics and gradually build up your skills.
3)Individuals who are graduated and working in the Data Science field and looking to upgrade their careers.

Course Objective

After the completion of this course, Trainee will:
We provide Classroom training on IBM Certified Data Science at Hyderabad for the individuals who believe hand-held training. We teach as per the Indian Standard Time (IST) with In-depth practical Knowledge on each topic in classroom training, 80 – 90 Hrs of Real-time practical training classes. There are different slots available on weekends or weekdays according to your choices. We are also available over the call or mail or direct interaction with the trainer for active learning.

Pre-requisites

1. Prior programming experience is desirable but not necessary along with familiarity with basic concepts like variables and scope, functions and flow control
2. Basic knowledge of object-oriented programming concepts is preferred but not mandatory

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:
1. Professionals aspiring to make a career out of Big Data Analytics utilizing Python
2. Software Professionals
3. Analytics Professionals
4. ETL Developers
5. Project Managers
6. Testing Professionals
7. Other professionals who are looking for a solid foundation on open-source general purpose scripting language also can opt this training

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 SCIENCE 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. SCIPY LINEAR ALGEBRA OPERATIONS INTERPOLATION NUMERICAL OPERATIONS FAST FOURIER TRANSFORM
    2. PANDASs

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. Dropping values
  15. Exporting and Importing Data

12. MATPLOT LIB:
Introduction:

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

13. STATISTICS AND PROBABILITY

    1. Statistical thinking in Python and approach of Data Analysis
    2. Fundamental statistics terms and its definitions
    3. Applying basic statistics in Python with NumPy
    4. Cumulative Distribution functions
    5. Modelling Distributions
    6. Graphical exploratory data analysis with Python
    7. Probability theories:
    8. Ranges, Mean, Variance, Standard Deviation and various distributions
    9. Mass and Density functions
    10. Kernel density estimation
    11. Understanding Bayes theorem and predictions*
    12. Estimation
    13. Sampling distributions, bias and Exponential distributions
    14. Hypothesis testing
    15. Hypothesis Test
    16. Testing Correlation and Proportions
    17. Chi-Squared Tests
    18. Errors, Power and Replications

14. MACHINE LEARNING

    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. Clustering
    11. k-Means, Dimension Reduction with PCA (Principal Component Analysis)
    12. Hierarchical Clusterings.

15. DEEP LEARNING:

    1. NEURAL NETWORKS AND TYPES
    2. TENSORFLOW AND KERAS
    3. 16. PROJECT

Prepare for Certification

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