World’s #1 Online Bootcamp

Data Science with Python Course Overview

The Python for Data Science Training in Melbourne course will help you apply Data Science methods and acquire essential data analysis skills and concepts related to Python programming. The Python for Data Science Course in Melbourne offers instruction in valuable subjects like Data Analysis, Data Visualization, Machine Learning, and NLP.

Data Science with Python Training Key Features

100% Money Back Guarantee
No questions asked refund*

At Simplilearn, we value the trust of our patrons immensely. But, if you feel that this Data Science with Python course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
  • 68 hours of blended learning
  • 4 industry-based projects
  • Interactive learning with Jupyter notebooks labs
  • Lifetime access to self-paced learning
  • Dedicated mentoring session from faculty of industry experts

Skills Covered

  • Data wrangling
  • Data exploration
  • Data visualization
  • Mathematical computing
  • Web scraping
  • Hypothesis building
  • Python programming concepts
  • NumPy and SciPy package
  • ScikitLearn package for Natural Language Processing

Benefits

The Python for Data Science Training in Melbourne course will be your guide to learning how to use Python. According to a latest US Bureau of Labor Statistics report, by 2026, 11.6 million jobs will be created in the field of data science and professional with the right set of skills and expertise, especially in Python stand to gain the most.

  • Designation
  • Annual Salary
  • Hiring Companies
  • Data Analyst
    Annual Salary
    $43KMin
    $62KAverage
    $95KMax
    Source: Glassdoor
    Hiring Companies
    Amazon hiring for Data Analyst professionals in Melbourne
    JPMorgan Chase hiring for Data Analyst professionals in Melbourne
    Genpact hiring for Data Analyst professionals in Melbourne
    VMware hiring for Data Analyst professionals in Melbourne
    LarsenAndTurbo hiring for Data Analyst professionals in Melbourne
    Citi hiring for Data Analyst professionals in Melbourne
    Accenture hiring for Data Analyst professionals in Melbourne
    Source: Indeed
  • Data Scientist
    Annual Salary
    $83KMin
    $113KAverage
    $154KMax
    Source: Glassdoor
    Hiring Companies
    Accenture hiring for Data Scientist professionals in Melbourne
    Oracle hiring for Data Scientist professionals in Melbourne
    Microsoft hiring for Data Scientist professionals in Melbourne
    Walmart hiring for Data Scientist professionals in Melbourne
    Amazon hiring for Data Scientist professionals in Melbourne
    Source: Indeed

Training Options

Self-Paced Learning

A$ 910

  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 4 hands-on projects to perfect the skills learnt
  • 3 simulation test papers for self-assessment
  • Lab access to practice live during sessions
  • 24x7 learner assistance and support

online Bootcamp

A$ 860

  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online classroom training by top instructors and practitioners
  • Classes starting in Melbourne from:-
4th Dec: Weekday Class
10th Dec: Weekday Class
Show all classes

Corporate Training

Customized to your team's needs

  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Data Science with Python Course Curriculum

Eligibility

The Python for Data Science training in Melbourne is the best course for those who want to jump into the field of Data Science. The course is useful for experienced software developers and IT professionals, not to mention anyone who just wants to learn about Data Science.
Read More

Pre-requisites

Taking the Python for Data Science course in Melbourne is something that should be looked into by professionals who have prior programming experience, and knowledge of related topics like Statistics Essentials for Data Science, Data Science in Python, Math Refresher, and Data Science in Real Life. These concepts will also be covered in the course.
Read More

Course Content

  • Data Science with Python

    Preview

    • Lesson 01: Course Introduction

      09:05Preview
      • 1.01 Course Introduction
        05:54
      • 1.02 Demo Jupyter Lab Walk - Through
        03:11

    • Lesson 02: Introduction to Data Science

      09:10Preview
      • 2.01 Learning Objectives
        00:27
      • 2.02 Data Science Methodology
        01:20
      • 2.03 From Business Understanding to Analytic Approach
        01:02
      • 2.04 From Requirements to Collection
        01:06
      • 2.05 From Understanding to Preparation
        01:10
      • 2.06 From Modeling to Evaluation
        01:53
      • 2.07 From Deployment to Feedback
        01:52
      • 2.08 Key Takeaways
        00:20

    • Lesson 03: Python Libraries for Data Science

      01:59:39Preview
      • 3.01 Learning Objectives
        00:34
      • 3.02 Python Libraries for Data Science
        01:51
      • 3.03 Import Library into Python Program
        01:05
      • 3.04 Numpy
        04:35
      • 3.05 Demo Numpy
        05:08
      • 3.06 Fundamentals of Numpy
        02:13
      • 3.07 Numpy Array Shapes and axes Part A
        02:48
      • 3.08 Numpy Array Shapes and axes Part B
        03:22
      • 3.09 Arithmetic Operations
        02:34
      • 3.10 Conditional Statements in Python
        02:44
      • 3.11 Common Mathematical and Statistical Functions in NumPy
        04:25
      • 3.12 Indexing and Slicing in Python Part A
        02:26
      • 3.13 Indexing and Slicing in Python Part B
        02:25
      • 3.14 Introduction to Pandas
        01:41
      • 3.15 Introduction to Pandas Series
        03:37
      • 3.16 Querying a Series
        03:54
      • 3.17 Pandas Dataframe
        02:53
      • 3.18 Introduction to Pandas Panel
        01:45
      • 3.19 Common Functions in Pandas
        02:20
      • 3.20 Statistical Functions in Pandas
        01:43
      • 3.21 Date and Timedelta
        02:18
      • 3.22 IO Tools
        02:36
      • 3.23 Categorical Data
        02:09
      • 3.24 Working with Text Data
        02:34
      • 3.25 Iteration
        01:54
      • 3.26 Plotting with Pandas
        03:23
      • 3.27 Matplotlib
        06:04
      • 3.28 Demo Matplotlib
        02:09
      • 3.29 Data Visualization Libraries in Python Matplotlib
        01:30
      • 3.30 Graph Types
        01:14
      • 3.31 Using Matplotlib to Plot Graphs
        03:32
      • 3.32 Matplotlib for 3d Visualization
        02:14
      • 3.33 Using Matplotlib with Other Python Packages
        01:02
      • 3.34 Data Visualization Libraries in Python Seaborn An Introduction
        00:58
      • 3.35 Seaborn Visualization Features
        02:13
      • 3.36 Using Seaborn to Plot Graphs
        01:40
      • 3.37 Analysis using seaborn plots
        00:53
      • 3.38 Plotting 3D Graphs for Multiple Columns using Seaborn
        03:16
      • 3.39 SciPy
        05:23
      • 3.40 Demo Scipy
        01:38
      • 3.41 Scikit-learn
        02:08
      • 3.42 Scikit Models
        01:25
      • 3.43 Scikit Datasets
        01:12
      • 3.44 Preprocessing Data in Scikit Learn Part 1
        01:28
      • 3.45 Preprocessing Data in Scikit Learn Part 2
        01:45
      • 3.46 Preprocessing Data in Scikit Learn Part 3
        02:04
      • 3.47 Demo Scikit learn
        06:20
      • 3.48 Key Takeaways
        00:34

    • Lesson 04: Statistics

      02:29:57Preview
      • 4.01 Learning Objectives
        00:34
      • 4.02 Introduction to Linear Algebra
        02:09
      • 4.03 Scalars and vectors
        01:27
      • 4.04 Dot product of Two Vectors
        02:02
      • 4.05 Linear Independence of Vectors
        00:46
      • 4.06 Norm of a Vector
        01:33
      • 4.07 Matrix
        02:46
      • 4.08 Matrix Operations
        02:38
      • 4.09 Transpose of a Matrix
        00:47
      • 4.10 Rank of a Matrix
        01:45
      • 4.11 Determinant of a matrix and Identity matrix or operator
        02:15
      • 4.12 Inverse of a matrix and Eigenvalues and Eigenvectors
        02:10
      • 4.13 Calculus in Linear Algebra
        01:14
      • 4.14 Importance of Statistics for Data Science
        02:00
      • 4.15 Common Statistical Terms
        01:19
      • 4.16 Types of Statistics
        02:10
      • 4.17 Data Categorization and types of data
        02:40
      • 4.18 Levels of Measurement
        02:04
      • 4.19 Measures of central tendency mean
        01:33
      • 4.20 Measures of Central Tendency Median
        01:37
      • 4.21 Measures of Central Tendency Mode
        01:03
      • 4.22 Measures of Dispersion
        01:56
      • 4.23 Variance
        02:14
      • 4.24 Random Variables
        01:36
      • 4.25 Sets
        02:03
      • 4.26 Measure of Shape Skewness
        01:38
      • 4.27 Measure of Shape Kurtosis
        01:20
      • 4.28 Covariance and corelation
        02:11
      • 4.29 Basic Statistics with Python Problem Statement
        00:49
      • 4.30 Basic Statistics with Python Solution
        10:30
      • 4.31 Probability its Importance and Probability Distribution
        02:49
      • 4.32 Probability Distribution Binomial Distribution
        02:13
      • 4.33 Binomial Distribution using Python
        01:31
      • 4.34 Probability Distribution Poisson Distribution
        02:08
      • 4.35 Poisson Distribution Using Python
        01:20
      • 4.36 Probability Distribution Normal Distribution
        03:17
      • 4.37 Probability Distribution Uniform Distribution
        01:03
      • 4.38 Probability Distribution Bernoulli Distribution
        02:27
      • 4.39 Probability Density Function and Mass Function
        01:57
      • 4.40 Cumulative Distribution Function
        01:52
      • 4.41 Central Limit Theorem
        02:22
      • 4.42 Bayes Theorem
        01:50
      • 4.43 Estimation Theory
        02:09
      • 4.44 Point Estimate using Python
        00:45
      • 4.45 Distribution
        01:11
      • 4.46 Kurtosis Skewness and Student's T- distribution
        01:46
      • 4.47 Hypothesis Testing and mechanism
        01:59
      • 4.48 Hypothesis Testing Outcomes Type I and II Errors
        01:28
      • 4.49 Null Hypothesis and Alternate Hypothesis
        01:27
      • 4.50 Confidence Intervals
        01:32
      • 4.51 Margin of Errors
        01:21
      • 4.52 Confidence Levels
        01:05
      • 4.53 T test and P values Using Python
        04:39
      • 4.54 Z test and P values Using Python
        05:25
      • 4.55 Comparing and Contrastin T test and Z-tests
        02:54
      • 4.56 Chi Squared Distribution
        02:32
      • 4.57 Chi Squared Distribution using Python
        03:18
      • 4.58 Chi squared Test and Goodness of Fit
        02:16
      • 4.59 ANOVA
        02:05
      • 4.60 ANOVA Terminologies
        01:31
      • 4.61 Assumptions and Types of ANOVA
        02:19
      • 4.62 Partition of Variance
        02:32
      • 4.63 F-distribution
        02:01
      • 4.64 F Distribution using Python
        03:54
      • 4.65 F-Test
        02:32
      • 4.66 Advanced Statistics with Python Problem Statement
        00:54
      • 4.67 Advanced Statistics with Python Solution
        10:06
      • 4.68 Key Takeaways
        00:38

    • Lesson 05: Data Wrangling

      31:32Preview
      • 5.01 Learning Objectives
        00:42
      • 5.02 Data Exploration Loading Files Part A
        02:53
      • 5.03 Data Exploration Loading Files Part B
        01:36
      • 5.04 Data Exploration Techniques Part A
        02:44
      • 5.05 Data Exploration Techniques Part B
        02:48
      • 5.06 Seaborn
        02:19
      • 5.07 Demo Correlation Analysis
        02:38
      • 5.08 Data Wrangling
        01:28
      • 5.09 Missing Values in a Dataset
        01:57
      • 5.10 Outlier Values in a Dataset
        01:50
      • 5.11 Demo Outlier and Missing Value Treatment
        04:12
      • 5.12 Data Manipulation
        00:49
      • 5.13 Functionalities of Data Object in Python Part A
        01:50
      • 5.14 Functionalities of Data Object in Python Part B
        01:34
      • 5.15 Different Types of Joins
        01:34
      • 5.16 Key Takeaways
        00:38

    • Lesson 06: Feature Engineering

      06:57
      • 6.01 Learning Objectives
        00:28
      • 6.02 Introduction to Feature Engineering
        01:50
      • 6.03 Encoding of Catogorical Variables
        00:27
      • 6.04 Label Encoding
        01:46
      • 6.05 Techniques used for Encoding variables
        02:11
      • 6.06 Key Takeaways
        00:15

    • Lesson 07: Exploratory Data Analysis

      24:58Preview
      • 7.01 Learning Objectives
        00:33
      • 7.02 Types of Plots
        09:38
      • 7.03 Plots and Subplots
        10:06
      • 7.04 Assignment 01 Pairplot Demo
        02:28
      • 7.05 Assignment 02 Pie Chart Demo
        01:52
      • 7.06 Key Takeaways
        00:21

    • Lesson 08: Feature Selection

      06:15Preview
      • 8.01 Learning Objectives
        00:33
      • 8.02 Feature Selection
        01:28
      • 8.03 Regression
        00:54
      • 8.04 Factor Analysis
        01:58
      • 8.05 Factor Analysis Process
        01:07
      • 8.06 Key Takeaways
        00:15
  • Free Course

  • Math Refresher

    Preview

    • Lesson 01: Course Introduction

      06:23Preview
      • 1.01 About Simplilearn
        00:28
      • 1.02 Introduction to Mathematics
        01:18
      • 1.03 Types of Mathematics
        02:39
      • 1.04 Applications of Math in Data Industry
        01:17
      • 1.05 Learning Path
        00:25
      • 1.06 Course Components
        00:16

    • Lesson 02: Probability and Statistics

      32:38Preview
      • 2.01 Learning Objectives
        00:29
      • 2.02 Basics of Statistics and Probability
        03:08
      • 2.03 Introduction to Descriptive Statistics
        02:12
      • 2.04 Measures of Central Tendencies​
        04:50
      • 2.05 Measures of Asymmetry
        02:24
      • 2.06 Measures of Variability​
        04:55
      • 2.07 Measures of Relationship​
        05:22
      • 2.08 Introduction to Probability
        08:36
      • 2.09 Key Takeaways
        00:42
      • 2.10 Knowledge check

    • Lesson 03: Coordinate Geometry

      06:31Preview
      • 3.01 Learning Objectives
        00:35
      • 3.02 Introduction to Coordinate Geometry​
        03:16
      • 3.03 Coordinate Geometry Formulas​
        01:51
      • 3.04 Key Takeaways
        00:49
      • 3.05 Knowledge Check

    • Lesson 04: Linear Algebra

      29:53Preview
      • 4.01 Learning Objectives
        00:29
      • 4.02 Introduction to Linear Algebra
        03:21
      • 4.03 Forms of Linear Equation
        05:21
      • 4.04 Solving a Linear Equation
        05:21
      • 4.05 Introduction to Matrices
        02:05
      • 4.06 Matrix Operations
        07:07
      • 4.07 Introduction to Vectors
        01:00
      • 4.08 Types and Properties of Vectors
        01:52
      • 4.09 Vector Operations
        02:39
      • 4.10 Key Takeaways
        00:38
      • 4.11 Knowledge Check

    • Lesson 05: Eigenvalues Eigenvectors and Eigendecomposition

      08:56Preview
      • 5.01 Learning Objectives
        00:29
      • 5.02 Eigenvalues
        01:19
      • 5.03 Eigenvectors
        04:09
      • 5.04 Eigendecomposition
        02:21
      • 5.05 Key Takeaways
        00:38
      • 5.06 Knowledge Check

    • Lesson 06: Introduction to Calculus

      09:47Preview
      • 6.01 Learning Objectives
        00:30
      • 6.02 Basics of Calculus
        01:20
      • 6.03 Differential Calculus
        03:01
      • 6.04 Differential Formulas
        01:01
      • 6.05 Integral Calculus
        02:33
      • 6.06 Integration Formulas
        00:47
      • 6.07 Key Takeaways
        00:35
      • 6.08 Knowledge Check
  • Free Course

  • Statistics Essential for Data Science

    Preview

    • Lesson 01: Course Introduction

      07:05Preview
      • 1.01 Course Introduction
        05:19
      • 1.02 What Will You Learn
        01:46

    • Lesson 02: Introduction to Statistics

      25:49Preview
      • 2.01 Learning Objectives
        01:16
      • 2.02 What Is Statistics
        01:50
      • 2.03 Why Statistics
        02:06
      • 2.04 Difference between Population and Sample
        01:20
      • 2.05 Different Types of Statistics
        02:42
      • 2.06 Importance of Statistical Concepts in Data Science
        03:20
      • 2.07 Application of Statistical Concepts in Business
        02:11
      • 2.08 Case Studies of Statistics Usage in Business
        03:09
      • 2.09 Applications of Statistics in Business: Time Series Forecasting
        03:50
      • 2.10 Applications of Statistics in Business Sales Forecasting
        03:19
      • 2.11 Recap
        00:46

    • Lesson 03: Understanding the Data

      17:29Preview
      • 3.01 Learning Objectives
        01:12
      • 3.02 Types of Data in Business Contexts
        02:11
      • 3.03 Data Categorization and Types of Data
        03:13
      • 3.03 Types of Data Collection
        02:14
      • 3.04 Types of Data
        02:01
      • 3.05 Structured vs. Unstructured Data
        01:46
      • 3.06 Sources of Data
        02:17
      • 3.07 Data Quality Issues
        01:38
      • 3.08 Recap
        00:57

    • Lesson 04: Descriptive Statistics

      34:51Preview
      • 4.01 Learning Objectives
        01:26
      • 4.02 Descriptive Statistics
        02:03
      • 4.03 Mathematical and Positional Averages
        03:15
      • 4.04 Measures of Central Tendancy: Part A
        02:17
      • 4.05 Measures of Central Tendancy: Part B
        02:41
      • 4.06 Measures of Dispersion
        01:15
      • 4.07 Range Outliers Quartiles Deviation
        02:30
      • 4.08 Mean Absolute Deviation (MAD) Standard Deviation Variance
        03:37
      • 4.09 Z Score and Empirical Rule
        02:14
      • 4.10 Coefficient of Variation and Its Application
        02:06
      • 4.11 Measures of Shape
        02:39
      • 4.12 Summarizing Data
        02:03
      • 4.13 Recap
        00:54
      • 4.14 Case Study One: Descriptive Statistics
        05:51

    • Lesson 05: Data Visualization

      23:36Preview
      • 5.01 Learning Objectives
        00:57
      • 5.02 Data Visualization
        02:15
      • 5.03 Basic Charts
        01:52
      • 5.04 Advanced Charts
        02:19
      • 5.05 Interpretation of the Charts
        02:57
      • 5.06 Selecting the Appropriate Chart
        02:25
      • 5.07 Charts Do's and Dont's
        02:47
      • 5.08 Story Telling With Charts
        01:29
      • 5.09 Data Visualization: Example
        02:41
      • 5.10 Recap
        00:50
      • 5.11 Case Study Two: Data Visualization
        03:04

    • Lesson 06: Probability

      21:51Preview
      • 6.01 Learning Objectives
        00:55
      • 6.02 Introduction to Probability
        03:10
      • 6.03 Probability Example
        02:02
      • 6.04 Key Terms in Probability
        02:25
      • 6.05 Conditional Probability
        02:11
      • 6.06 Types of Events: Independent and Dependent
        02:59
      • 6.07 Addition Theorem of Probability
        01:58
      • 6.08 Multiplication Theorem of Probability
        02:08
      • 6.09 Bayes Theorem
        03:10
      • 6.10 Recap
        00:53

    • Lesson 07: Probability Distributions

      24:45Preview
      • 7.01 Learning Objectives
        00:52
      • 7.02 Probability Distribution
        01:25
      • 7.03 Random Variable
        02:21
      • 7.04 Probability Distributions Discrete vs.Continuous: Part A
        01:44
      • 7.05 Probability Distributions Discrete vs.Continuous: Part B
        01:45
      • 7.06 Commonly Used Discrete Probability Distributions: Part A
        03:18
      • 7.07 Discrete Probability Distributions: Poisson
        03:16
      • 7.08 Binomial by Poisson Theorem
        02:28
      • 7.09 Commonly Used Continuous Probability Distribution
        03:22
      • 7.10 Application of Normal Distribution
        02:49
      • 7.11 Recap
        01:25

    • Lesson 08: Sampling and Sampling Techniques

      36:45Preview
      • 8.01 Learnning Objectives
        00:51
      • 8.02 Introduction to Sampling and Sampling Errors
        03:05
      • 8.03 Advantages and Disadvantages of Sampling
        01:31
      • 8.04 Probability Sampling Methods: Part A
        02:32
      • 8.05 Probability Sampling Methods: Part B
        02:27
      • 8.06 Non-Probability Sampling Methods: Part A
        01:42
      • 8.07 Non-Probability Sampling Methods: Part B
        01:25
      • 8.08 Uses of Probability Sampling and Non-Probability Sampling
        02:08
      • 8.09 Sampling
        01:08
      • 8.10 Probability Distribution
        02:53
      • 8.11 Theorem Five Point One
        00:52
      • 8.12 Center Limit Theorem
        02:14
      • 8.13 Sampling Stratified: Sampling Example
        04:35
      • 8.14 Probability Sampling: Example
        01:17
      • 8.15 Recap
        01:07
      • 8.16 Case Study Three: Sample and Sampling Techniques
        05:16
      • 8.17 Spotlight
        01:42

    • Lesson 09: Inferential Statistics

      37:08Preview
      • 9.01 Learning Objectives
        01:04
      • 9.02 Inferential Statistics
        03:09
      • 9.03 Hypothesis and Hypothesis Testing in Businesses
        03:24
      • 9.04 Null and Alternate Hypothesis
        01:44
      • 9.05 P Value
        03:22
      • 9.06 Levels of Significance
        01:16
      • 9.07 Type One and Two Errors
        01:37
      • 9.08 Z Test
        02:24
      • 9.09 Confidence Intervals and Percentage Significance Level: Part A
        02:52
      • 9.10 Confidence Intervals: Part B
        01:20
      • 9.11 One Tail and Two Tail Tests
        04:43
      • 9.12 Notes to Remember for Null Hypothesis
        01:02
      • 9.13 Alternate Hypothesis
        01:51
      • 9.14 Recap
        00:56
      • 9.15 Case Study 4: Inferential Statistics
        06:24
      • Hypothesis Testing

    • Lesson 10: Application of Inferential Statistics

      27:20Preview
      • 10.01 Learning Objectives
        00:50
      • 10.02 Bivariate Analysis
        02:01
      • 10.03 Selecting the Appropriate Test for EDA
        02:29
      • 10.04 Parametric vs. Non-Parametric Tests
        01:54
      • 10.05 Test of Significance
        01:38
      • 10.06 Z Test
        04:27
      • 10.07 T Test
        00:54
      • 10.08 Parametric Tests ANOVA
        03:26
      • 10.09 Chi-Square Test
        02:31
      • 10.10 Sign Test
        01:58
      • 10.11 Kruskal Wallis Test
        01:04
      • 10.12 Mann Whitney Wilcoxon Test
        01:18
      • 10.13 Run Test for Randomness
        01:53
      • 10.14 Recap
        00:57

    • Lesson 11: Relation between Variables

      20:07Preview
      • 11.01 Learning Objectives
        01:06
      • 11.02 Correlation
        01:54
      • 11.03 Karl Pearson's Coefficient of Correlation
        02:36
      • 11.04 Karl Pearsons: Use Cases
        01:30
      • 11.05 Correlation Example
        01:59
      • 11.06 Spearmans Rank Correlation Coefficient
        02:14
      • 11.07 Causation
        01:47
      • 11.08 Example of Regression
        02:28
      • 11.09 Coefficient of Determination
        01:12
      • 11.10 Quantifying Quality
        02:29
      • 11.11 Recap
        00:52

    • Lesson 12: Application of Statistics in Business

      17:25Preview
      • 12.01 Learning Objectives
        00:53
      • 12.02 How to Use Statistics In Day to Day Business
        03:29
      • 12.03 Example: How to Not Lie With Statistics
        02:34
      • 12.04 How to Not Lie With Statistics
        01:49
      • 12.05 Lying Through Visualizations
        02:15
      • 12.06 Lying About Relationships
        03:31
      • 12.07 Recap
        01:06
      • 12.08 Spotlight
        01:48

    • Lesson 13: Assisted Practice

      11:47Preview
      • Assisted Practice: Problem Statement
        02:10
      • Assisted Practice: Solution
        09:37

Contact Us

1-800-982-536

( Toll Free )

Industry Project

  • Project 1

    Products rating prediction for Amazon

    Help Amazon, a US-based e-commerce company, improve its recommendation engine by predicting ratings for the non-rated products and adding them to recommendations accordingly.

    Products rating prediction for Amazon
  • Project 2

    Demand Forecasting for Walmart

    Predict accurate sales for 45 Walmart stores, considering the impact of promotional markdown events. Check if macroeconomic factors have an impact on sales.

    Demand Forecasting for Walmart
  • Project 3

    Improving customer experience for Comcast

    Provide Comcast, a US-based global telecom company, key recommendations to improve customer experience by identifying and improving problem areas that lower customer satisfaction.

    Improving customer experience for Comcast
  • Project 4

    Attrition Analysis for IBM

    IBM, a leading US-based IT company, wants to identify the factors that influence employee attrition by building a logistics regression model that can help predict employee churn.

    Attrition Analysis for IBM
  • Project 5

    NYC 311 Service Request Analysis

    Perform a service request data analysis of New York City 3-1-1 calls. Focus on data wrangling techniques to understand patterns in the data and visualize the major complaint types.

    NYC 311 Service Request Analysis
  • Project 6

    MovieLens Dataset Analysis

    A research team is working on information filtering, collaborative filtering, and recommender systems. Perform analysis using Exploratory Data Analysis technique for user datasets.

    MovieLens Dataset Analysis
prevNext

Data Science with Python Exam & Certification

Data Science with Python Training in Melbourne, Australia

  • Who provides the certification and how long is it valid for?

    After a student successfully passes the course Python for Data Science training in Melbourne, they recieve from Simplilearn a Data Science course in Melbourne certificate of completion recognized across the industry and featuring no expiration date.

  • What do I need to unlock my Simplilearn certificate?

    The online classroom or online self-learning are the two options available to complete the Python for Data Science training in Melbourne course. To unlock the Simplilearn certificate for:

    Online Classroom:

    • Complete one whole batch of this Python for Data Science course in Melbourne. 
    • Submit at least one completed project

    Online Self-Learning:

    • Complete 85% of the course
    • Submit at least one completed project

  • Do you provide any practice tests as part of the Data Science Python Course?

    If you'd like to see what the certification test is like, take the practice test we offer for free, including in your Python Data Science training in Melbourne. This test will help in preparation for the actual certification exam. Candidates can also try the Free Data Science with Python Practice Test to learn more about the type of tests that are part of the Python for Data Science course in Melbourne curriculum.

Data Science with Python Course Reviews

  • Mushtaque Ansari

    Mushtaque Ansari

    Senior Software Developer, Bangalore

    I had a wonderful experience learning Data Science with Python with Simplilearn. Thank you, Vaishali for explaining concepts theoretically and practically. The live sessions helped me easily understand the concepts.

  • Vignesh Manikandan

    Vignesh Manikandan

    Bangalore

    The online classes were well-paced and helped us learn a ton of stuff within a short amount of time. Vaishali is very knowledgeable and handled all the sessions with outstanding professionalism. Thanks for your expertise.

  • Arvind Kumar

    Arvind Kumar

    Technology Lead, Nagpur

    It was a great learning experience. My trainer, Vaishali delivered each session well. All topics were explained with in-depth theory, real-time examples, and execution of the same in Python. Her teaching methodology enhanced the learning process.

  • Brian

    Brian

    Program Manager (iGPM RBEI), Bangalore

    The training was well-structured, and the trainer was experienced with hands-on know-how. The trainer handled responses and queries efficiently with a good amount of patience.

  • Darshan Gajjar

    Darshan Gajjar

    Gandhinagar

    I learned a lot about Python, Numpy, Pandas, Visualization. The instructor, Swagat was excellent in explaining things clearly. The support team is also accommodative and resolves issues instantly.

  • Aashish Kumar

    Aashish Kumar

    Patna

    I completed this course at Simplilearn. The faculty, Prashanth Nair, was extremely knowledgeable, and the entire class appreciated his way of teaching. Simplilearn's support team was very accommodating and quick in providing responses. I was able to grab a 30% hike in my salary after getting certified.

  • Nikhil Lohakare

    Nikhil Lohakare

    Pune

    The sessions are very interesting and easy to understand. I enjoyed each and every one of them, thanks to the trainer, Prashant.

  • Dastagiri Durgam

    Dastagiri Durgam

    Hyderabad

    Incredible mentorship, and amazing and unique lectures. Simplilearn provides a great way to learn with self-paced videos and recordings of online sessions. Thanks, Simplilearn, for providing quality education.

  • Mukesh Pandey

    Mukesh Pandey

    Hyderabad

    Simplilearn is an excellent platform for online learning. Their course curriculum is comprehensive and up to date. We get lifetime access to the recorded sessions in case we need to refresh our understanding. If you are looking to upskill, I suggest you sign up with Simplilearn. They offer classes in almost all disciplines.

  • C Muthu Raman

    C Muthu Raman

    Pune

    Simplilearn facilitates a brilliant platform to acquire new & relevant skills at ease. Well laid out course content and expert faculty ensure an excellent learning experience.

  • Surendaran Baskaran

    Surendaran Baskaran

    Coimbatore

    I took this course with Simplilearn. The instructor is knowledgeable and shares their skills and knowledge. My learning experience has been outstanding with Simplilearn. The practice labs and materials are helpful for better learning. Thank you, Simplilearn. Happy Learning!!

  • Akash Raj

    Akash Raj

    Technology Engineer, Bangalore

    The instructor not only delivers the lecture but also focuses on practical aspects related to the subject. This is something about the course that really impressed me.

  • Shiv Sharma

    Shiv Sharma

    Mumbai

    Prashant Nair is an awesome faculty. The way he simplifies, relates and explains topics is outstanding. I would love to enroll for and attend all his classes.

  • Shweta Chauhan

    Shweta Chauhan

    Bangalore

    Thanks a lot, Sunny, for the immense support and guidance throughout the project, and for your patience while calmly helping me fix both small and big problems. You have excellent and in-depth knowledge about Python and the alternative options you taught me. I'm delighted to share my opinion about my experience.

  • Satabdi Adhikary

    Satabdi Adhikary

    Bangalore

    Simplilearn's courses are affordable and helped me learn something new during the lockdown. Moreover, I also got to add a Well-Known Global Name like Simplilearn to my resume. I could choose the trainer as well as enroll for multiple sessions using the Flexible Pass.

prevNext

Why Online Bootcamp

  • Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
  • Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

Data Science with Python Training FAQs

  • Why learn Python for Data Science?

    Python is the most popular programming language for Data Science. Python is widely used to perform data analysis, data manipulation, and data visualization. The advantages of using Python for data science are:

    • Python offers access to a wide variety of Data Science libraries and it is the ideal language for implementing algorithms and the rapid development of applications in Data Science.
    • Python is an object-oriented programming language with integrated dynamic semantics, used primarily for application and web development. The widely used language offers dynamic binding and dynamic typing options.
    • Python is a high-level programming language with an enormous community. Its flexibility is quite useful for any issues related to application development in Data Science.

  • Can I learn Python Data Science course online?

    The rapid evolution of learning methodologies, thanks to the influx of technology, has increased the ease and efficiency of online learning, making it possible to learn at your own pace. Simplilearn's Python Data Science course provides live classes and access to study materials from anywhere and at any time. Our extensive (and growing) collection of blogs, tutorials, and YouTube videos will help you get up to speed on the main concepts. Even after your class ends, we provide a 24/7 support system to help you with any questions or concerns you may have.

  • What is the job outlook for Data Science with Python programming professionals?

    Harvard Business Review has already named Data Scientist as the ‘Sexiest Job of the 21st Century.’ The statement is echoed in LinkedIn Emerging Jobs Report 2021 in which Data Science specialists are one of the top emerging jobs in the US with Python as one of its key skills. The job role has witnessed an annual growth of 35 percent for Data scientists and Data engineers.

  • Do I need coding experience to learn Python for Data Science?

    If you have prior coding experience or familiarity with any other object-oriented programming language, it will be easier for you to learn Python for Data Science. However, it is not compulsory.

  • I have familiarity in other programming languages like C++/Java. Will the Data Science with Python course help me to switch to Python?

    Python has simple syntax and is easy to understand. Knowledge of Java or C++ language helps in learning Python faster. This is because Python is also object-oriented and many of its prototypes are similar to Java. So you can easily migrate to Python with this comprehensive course.

  • How much Python is required for Data Science?

    Python is used for a variety of applications and you don’t need to be familiar with all of its libraries and modules. Even if you know the basics of Python, this Data Science with Python certification covers the popular libraries of Python that are used in data science projects.

  • Does Python support any open-source libraries?

    Yes, Python supports a lot of open-source libraries like SciPy, NumPy, Scikit-Learn, TensorFlow, Matplotlib, and Pandas.

  • Does the knowledge imparted through this Data Science with Python certification apply to Machine Learning and Data Science projects?

    Yes, our Data Science with Python course is specifically designed to impart industry-oriented skills. The course material, practice with integrated labs, and real-world projects enhance your practical knowledge and help you apply them to Data Science projects.

  • How can I get started with this Data Science with Python course?

    It is beneficial if you brush up your skills in core math, statistics, and programming basics to get started with this Data Science with Python course.

  • Which companies use Python for Data Science?

    Major companies like Google, Instagram, Goldman Sachs, Facebook, Quora, Netflix, Dropbox, and PayPal use Python for Data Science.

  • How do Data Scientists use Python in daily work?

    Data scientists handle a variety of tasks in their day-to-day routine. They gather, merge, and analyze data and identify trends and patterns. They also build and test new algorithms to simplify data problems. Python is used along with other tools to perform all these tasks.

  • What are the system requirements to install Python for Data Science?

    To run Python, your system must fulfill the following basic requirements:

    • 32 or 64-bit Operating System
    • 1GB RAM 

    The instruction uses Anaconda and Jupyter notebooks. The e-learning videos provide detailed instructions on how to install them.

  • Who are our instructors and how are they selected?

    All of our highly qualified Data Science trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

  • What are the modes of training offered for this Python Data Science course?

    Live Virtual Classroom or Online Classroom: In online classroom training, you have the convenience of attending the Python Data Science course remotely from your desktop via video conferencing to enhance your productivity and reduce the time spent away from work or home.
     
    Online Self-Learning: In this mode, you will receive lecture videos and can proceed through the course at your convenience.
     
    WinPython portable distribution is the open-source environment on which all hands-on exercises will be performed. Instructions for installation will be given during the training.

  • Is this live training, or will I watch pre-recorded videos?

    If you enroll in the self-paced e-learning training program, you will have access to pre-recorded videos. However, if you enroll for the Online Classroom Flexi-Pass, you will have access to both instructor-led Data Science with Python training conducted online as well as the pre-recorded videos.

  • What if I miss a class?

    Simplilearn provides recordings of each class so you can review them as needed before the next session.

  • Can I cancel my enrollment? Will I get a refund?

    Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.

  • Are there any group discounts for classroom training programs?

    Yes, we have group discount packages for classroom training programs. Contact Help & Support to learn more about group discounts.

  • How do I enroll for Python Data Science course?

    You can enroll for this Data Science with Python certification training on our website and make an online payment using any of the following options: 
    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal 
    Once payment is received you will automatically receive a payment receipt and access information via email.

  • Whom should I contact to learn more about this Python Data Science course?

    Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide you with more details.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in Data Science on your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your Python Data Science course with us.

  • What is the recommended learning path after completing Data Science with Python course?

    You can either enroll in our Data Scientist Course or if you are looking to get a University certificate, you can enroll in the Professional Certificate Program in Data Science.

  • Disclaimer

    The projects have been built leveraging real publicly available data-sets of the mentioned organizations.

Related Programs

Find Big Data Analytics Programs in Melbourne

Post Graduate Program in Data ScienceData ScientistData AnalystTableau Certification TrainingPower BI
  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.