Learn Python Language Nexacu

Data Analysis in Python Course Indonesia

Learn Python for data analysis, instructor-led course.

Build on the fundamentals taught in our Python Basics course and start to achieve more with Data Analysis using Python. Our experienced trainer will teach you how to manipulate and visualize data. Create custom functions, plot with Matplotlib and create a linear model with scikit-learn. Get a handle on how to use Python and start exploring the freely available tools at your disposal.

Our experienced trainer will deliver this courses live online, answering your questions along the way. Read our full course outline below.
All classes delivered remotely, join us live online in Indonesia.

Data Analysis in Python Course Indonesia

Learn Python Language Nexacu
Learn Python for Data Analysis. Instructor-led training, remote courses in Indonesia.
Rp 7.500K
10 max
1 day

Frequently Asked Questions

Meet your Trainer

trainer Tamara

Tamara Shatar holds a PhD in Agricultural Data Science. She has extensive experience, including many years working as a research scientist focused on data analysis, modelling using machine learning, simulation and other techniques. While working in both academia and at the Australian CSIRO, part of her role included teaching a variety of data analysis skills. She is consistently well reviewed by her students. 

"Great trainer, structure, material and manual. I have completed programming courses before but this one just made more sense! I'm sure it was the trainer. Looking forward to the Intermediate course when it is available." -Using R Beginner Sydney, Australia

Is Python compatible with Microsoft?

Python can be used within a number of Microsoft products. In Power BI, reusable Python scripts can be used to manipulate data and create visualizations. In Azure, machine learning models can be written in Python and trained and deployed within the Azure Machine Learning Workspace.

What is Remote Training?

Remote training at Nexacu, means our team of experienced trainers will deliver your training virtually. With remote learning students can access our usual classroom training courses via video conferencing, ask questions, participate in discussion and share their screen with the trainer if they need help at any point in the course. Students have the same level of participation and access to the trainer as they would in classroom training sessions. 

Data Analysis in Python Indonesia Course Details

Price
Rp 7.500K
Duration
1 day
Times
9am - 4pm ( approx )
Class Size (max)
10
Class Size (avg)
5
Reference Materials
Provided
CPD Hours
6 hours
Delivery
Instructor-led training
Live Online
Address
Scientia Business Park Tower II
2th Floor Jalan Boulevad
Gading Serpong Block 0/2
Serpong
microsoft_gold
deloite_logo

Python Course Outlines

  • Data Analysis in Python
  • Python Intermediate
  • Python Certification
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Python Training Courses

4,72 5 Python Training Courses
4,72 out of 5
31197 Student Reviews

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What do I need to know to attend?

Students should have completed our Python Basics course or be familiar with the content covered in it. You should have a foundational knowledge of Python and pandas.

Data Analysis in Python Indonesia Learning Outcomes

  • use the extensive data manipulation capabilities of pandas DataFrames
  • customise the display of the output in Jupyter Notebooks
  • use the plotting capabilities of Matplotlib to plot distributions and bar charts
  • use the data visualisation library, Seaborn, and
  • fit a basic model using scikit-learn.

Data Analysis in Python Indonesia Course Content

  • Introduction
  • Data Analysis in Python

 

  • User-Defined Functions in Python
  • Function basics
  • Parameters
  • Positional vs keyword arguments
  • Defining a function
  • Indentation
  • User-Defined Functions in Python (cont'd)
  • Scope
  • *args and **kwargs
  • Unpacking operators
  • Lambda expressions
  • Conditional expressions
  • List comprehensions

 

  • Modify the DataFrame Display
  • pandas options
  • Working with pandas styles
  • Applying a style that is not dependent on values
  • Formatting values
  • String formats
  • Applying a style that is dependent on values
  • Built-in conditional formatting

 

  • Export Notebook as
  • Export to PDF or HTML
  • Create slides
  • Copy vs View
  • Setting with copy warning
  • Working with Missing Values
  • Missing values
  • inf and -inf
  • Removing missing values
  • Replacing missing values

 

  • Importing Data
  • Importing into a pandas DataFrame

 

  • Manipulating Data
  • Summarise a dataset
  • Report and display multiple summary statistics
  • Ordering data
  • Manipulating Data (cont'd)
  • Working with dates
  • Add columns with assign()
  • Working with strings
  • Reordering and dropping columns
  • Manipulating Data (cont'd)
  • Selecting rows based on values
  • Grouping and summarising data
  • Replacing values
  • Concatenate data
  • Bin continuous variables into categories

 

  • Working with Relational Data
  • Joining data from two DataFrames

 

  • Visualising Distributions
  • Visual representation of distributions with Matplotlib and Seaborn
  • Histograms
  • Boxplots
  • Bar and column charts

 

  • Multivariate Analysis
  • Scatterplot matrix
  • Bar and column charts

 

  • Basic Modelling
  • Create a linear model with scikit-learn

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