Business Analytics With Python

Objective
Are you ready to fast track your career in the field of data science? Start by learning the fundamentals of programming in Python and gaining an in-depth understanding of how to use the skill to extract information and knowledge from data.Python is the most popular language used in the field of data science. Even industry giants like Google and Netflix use it to generate insights and build better products. It can be quickly learnt and is versatile, making life easy for people who work with tonnes of data. 1.21GWS comprehensive certificate in Business Analytics using Python is tailored to train you on all aspects of Business Analytics; starting from exploratory data analysis, statistical and quantitative analysis, testing analytics models and forecasting through predictive modelling using Python & Microsoft Excel. The course will elucidate some of the most complex statistical tools used for data analysis through live online sessions by experts, real-life case study demonstrations, and videos.
Who should attend:
Professionals and students from various backgrounds such as statistics, marketing, finance, economics, IT, analytics, marketing research, etc. looking to make a career in analytics and data sciences.
REGISTER
GROUP OF 3 OR MORE [USD 800 PER PARTICIPANT]
INDIVIDUAL STANDARD PRICE [USD 1000 PER PARTICIPANT]
Course Features
- Lectures 100
- Quizzes 0
- Skill level All levels
- Language English
- Students 91
- Certificate No
- Assessments Yes
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Python Overview
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Python Data Structures
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Python Operators
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Python Decision Making
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Python Loops
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Python Loop Control Statements
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Python Toolbox
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Python Packages
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Data Manipulation
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Python Testing and Debugging
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Visualization using Python
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Introduction to Statistics and Probability
- Introduction to Statistics
- Descriptive Statistics
- Measure of Central Tendency
- Measure of Dispersion
- Measure of Shapes
- Probability
- Sampling methods
- Probability Sampling
- Simple Random Sampling
- Semantic Sampling
- Stratified Sampling
- Cluster Sampling
- Non- Probability Sampling
- Convinced Sampling
- Quota Sampling
- Judgement Sampling
- Snow Ball Sampling
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Introduction to Hypothetical Testing
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Introduction to Machine Learning
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Introduction to Modelling packages
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Introduction to Supervised & Unsupervised Learning
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Introduction to Time Series Analysis
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Data Visualization with Tableau