Learn advanced Python skills for data analysis, visualizations, and big data processing.

This course explores using Python for data scientists to perform exploratory data analysis, complex visualizations, and large-scale distributed processing using Big Data. In this course you’ll learn about essential mathematical and statistics libraries such as NumPy, Pandas, SciPy, SciKit-Learn, along with frameworks like TensorFlow and Spark. It also covers visualization tools like matplotlib, PIL, and Seaborn.

Who Should Attend?

Data Scientists, Data Engineers, and Software Engineers who are experienced with basic Python and data science.

We can organize this training at your preferred date and location. Contact Us!

Prerequisites

Before attending this course, you should have:

A solid data analytics and data science background
Python experience

Topics are covered in-depth and are geared for experienced students who have taken one of the prerequisite courses below or have practical hands-on experience.

What You Will Learn

Join an engaging hands-on learning environment, where you’ll learn:

How to work with Python in a Data Science context
How to use NumPy, Pandas, and MatPlotLib
How to create and process images with PIL
How to visualize with Seaborn
Key features of SciPy and SciKit Learn
How to interact with Spark using DataFrames
How to use SparkSQL, MLlib, and Big Data streaming

This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.

Training Outline

Python Review

Python Language
Essential Syntax
Lists, Sets, Dictionaries, and Comprehensions
Functions
Classes, Modules, and imports
Exceptions

iPython

iPython basics
Terminal and GUI shells
Creating and using notebooks
Saving and loading notebooks
Ad hoc data visualization
Web Notebooks (Jupyter)

NumPy

NumPy basics
Creating arrays
Indexing and slicing
Large number sets
Transforming data
Advanced tricks

SciPy

What can SciPy do?
Most useful functions
Curve fitting
Modeling
Data visualization
Statistics

SciPy subpackages

Clustering
Physical and mathematical Constants
FFTs
Integral and differential solvers
Interpolation and smoothing
Input and Output
Linear Algebra
Image Processing
Distance Regression
Root-finding
Signal Processing
Sparse Matrices
Spatial data and algorithms
Statistical distributions and functions
C/C++ Integration

pandas

pandas overview
Dataframes
Reading and writing data
Data alignment and reshaping
Fancy indexing and slicing
Merging and joining data sets

matplotlib

Creating a basic plot
Commonly used plots
Ad hoc data visualization
Advanced usage
Exporting images

The Python Imaging Library (PIL)

PIL overview
Core image library
Image processing
Displaying images

seaborn

Seaborn overview
Bivariate and univariate plots
Visualizing Linear Regressions
Visualizing Data Matrices
Working with Time Series data

SciKit-Learn Machine Learning Essentials

SciKit overview
SciKit-Learn overview
Algorithms Overview
Classification, Regression, Clustering, and Dimensionality Reduction
SciKit Demo

TensorFlow Overview

TensorFlow overview
Keras
Getting Started with TensorFlow

PySpark Overview

Python and Spark
SciKit-Learn vs. Spark MLlib
Python at Scale
PySpark Demo

RDDs and DataFrames

DataFrames and Resilient Distributed Datasets (RDDs)
Partitions
Adding variables to a DataFrame
DataFrame Types
DataFrame Operations
Dependent vs. Independent variables
Map/Reduce with DataFrames

Spark SQL

Spark SQL Overview
Data stores: HDFS, Cassandra, HBase, Hive, and S3
Table Definitions
Queries

Spark MLib

MLib overview
MLib Algorithms Overview
Classification Algorithms
Regression Algorithms
Decision Trees and forests
Recommendation with ALS
Clustering Algorithms
Machine Learning Pipelines
Linear Algebra (SVD, PCA)
Statistics in MLib

Spark Streaming

Streaming overview
Integrating Spark SQL, MLlib, and Streaming

Why Choose Us

Experience live, interactive learning from the comfort of your home or office with Bilginç IT Academy's Online Instructor-Led Next Level Python for Data Science Training in Switzerland. Engage directly with expert trainers in a virtual environment that mirrors the energy and schedule of a physical classroom.

Live Sessions: Join scheduled classes with a live instructor and other delegates in real-time.
Interactive Experience: Engage in group activities, hands-on labs, and direct Q&A sessions with your trainer and peers.
Global Expert Trainers: Learn from a handpicked global pool of expert trainers with deep industry experience.
Proven Expertise: Benefit from over 30 years of quality training experience, equipping you with lasting skills for success.
Scalable Delivery: Accessible worldwide, including Switzerland, with flexible scheduling to meet your professional needs.

Immerse yourself in our most sought-after learning style for Next Level Python for Data Science Training in Switzerland. Our hand-picked classroom venues in Switzerland offer an invaluable human touch, providing a focused and interactive environment for professional growth.

Highly Experienced Trainers: Boost your skills with trainers boasting 10-20+ years of real-world experience.
State-of-the-Art Venues: Learn in high-standard facilities designed to ensure a comfortable and distraction-free experience.
Small Class Sizes: Our limited class sizes foster meaningful discussions and a personalized learning journey.
Best Value: Achieve your certification with high-quality training and competitive pricing.

Streamline your organization's training requirements with Bilginc IT Academy’s Onsite Next Level Python for Data Science Training in Switzerland. Experience expert-led learning at your own business premises, tailored to your corporate goals.

Tailored Learning Experience: Customize the training content to fit your unique business projects or specific technical needs.
Maximize Training Budget: Eliminate travel and accommodation costs, focusing your entire budget on the training itself.
Team Building Opportunity: Enhance team bonding and collaboration through shared learning experiences in your workspace.
Progress Monitoring: Track and evaluate your employees' progression and performance with relative ease and direct oversight.

Why have you chosen us?

I have attended a training from Bilginc IT Academy before and I was satisfied.

I have attended a training from a different provider and it was not helpful.

Other

How many employees do you have in your IT department?

0 – 50

50 – 250

250 – 1000

1000+

Next Level Python for Data Science Training in Switzerland

Who Should Attend?

Prerequisites

What You Will Learn

Training Outline

Why Choose Us

Online Instructor-Led Training (VILT)

Classroom Based Learning

Onsite / In-House Learning

Available Training Dates