Intermediate Python for Data Science | Explore NumPy, Pandas, SciKit Learn, SciPy, TensorFlow & More (TTPS4876)
Geared for experienced Python users with basic data science skills, Next Level Python for Data Science is a comprehensive hands-on course that deep dives the advanced skills and tools used to perform exploratory data analysis, create complex visualizations, and perform large-scale distributed processing on Big Data.
Throughout the course, guided by our expert instructor, you’ll learn, gain the advanced skills required to leverage Python to effectively solve real-world problems and contribute to data-driven projects in a professional setting. Working in a workshop style, hands-on environment, you’ll hone your skills in numerical operations using NumPy and delve into advanced data manipulation techniques with Pandas. From applying complex mathematical functions in SciPy to mastering data visualization through Matplotlib and Seaborn, this program equips you for a broad spectrum of data science tasks. You’ll also get practical experience in merging, joining, and concatenating data sets, while gaining an understanding of machine learning fundamentals via scikit-learn. These technical abilities are framed within a problem-solving context, empowering you to contribute effectively to data-driven initiatives in your professional role.
With these advanced Python and data science skills, you’ll be equipped to lead complex data analysis projects that transform raw data into actionable insights for strategic decision-making. You’ll also have the capability to design and implement machine learning models, allowing your organization to harness the power of predictive analytics for enhanced operational efficiency and competitive advantage. You’ll exit this course with advanced skills tailored specifically for applications in data science, able to handle complex data sets, understand machine learning algorithms, and translate data into actionable insights.
- Price: $2,595.00
- Duration: 5 Days
- Delivery Methods: Virtual
| Start Date | Class Times | Price | Enroll |
|---|---|---|---|
| 08/19/2024 | 9:00 AM – 5:00 PM CT | $2,595.00 | |
| 10/14/2024 | 9:00 AM – 5:00 PM CT | $2,595.00 | |
| 12/09/2024 | 9:00 AM – 5:00 PM CT | $2,595.00 |
| Start Date | Class Times | Price | Enroll |
|---|---|---|---|
| 08/19/2024 | 9:00 AM – 5:00 PM CT | $2,595.00 | |
| 10/14/2024 | 9:00 AM – 5:00 PM CT | $2,595.00 | |
| 12/09/2024 | 9:00 AM – 5:00 PM CT | $2,595.00 |
Why choose TOPTALENT?
- Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
- Access an expansive range of over 3,000 training courses with a strong focus on Information Technology, Business Applications, and Leadership Development.
- Have confidence in an exceptional 95% approval rating from our students, reflecting outstanding satisfaction with our course content, program support, and overall customer service.
- Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.
Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.
Python Review (Optional)
- An overview of machine learning models
- The scikit-learn modules for different models
- Data representation in scikit-learn
- Supervised learning – classification and regression
- Unsupervised learning – clustering and dimensionality reduction
- Measuring prediction performance
Machine Learning with scikit-learn
- How to Install Pillow
- How to Load and Display Images
- How to Convert Images to NumPy Arrays and Back
- How to Save Images to File
- How to Resize Images
- How to Flip, Rotate, and Crop Images
- Extensions
Using PIL/Pillow
- A crash course in Matplotlib
- Covariance and correlation
- Conditional probability
- Bayes’ theorem
Visualization Using Matplotlib
- Introducing the data sets
- Concatenating data sets
- Missing values in concatenated DataFrames
- Left joins
- Inner joins
- Outer joins
- Merging on index labels
- Coding challenge
Merging, Joining and Concatenating
- Optimizing a data set for memory use
- Filtering by a single condition
- Filtering by multiple conditions
- Filtering by condition
- Dealing with duplicates
- Coding challenge
Filtering a DataFrame
- Overview of a DataFrame
- Similarities between Series and DataFrames
- Sorting by index
- Setting a new index
- Selecting columns and rows from a DataFrame
- Selecting rows from a DataFrame
- Extracting values from Series
- Renaming columns or rows
- Resetting an index
The DataFrame Object
- Data in the 21st century
- Introducing pandas
- A tour of pandas
- Summary
Next-Level Pandas
- Cluster
- Constants
- FFTpack
- Integrate
- Interpolate
- Linalg
- Ndimage
- Spatial
SciPy
- NumPy arrays
- Array functions
- Data processing using arrays
- Linear algebra with NumPy
- NumPy random numbers
NumPy Arrays and Vectorized Computation
- Why Python?
- Python syntax compared to other programming languages
- Python interpreter
- Strings
- Understanding lists
- Tuples and Sets
- Dictionaries
- Parsing command-line arguments
- Decision making
- Loops
- Iterators
- Generators
- Functions & Modules
Optional: Working with TensorFlow
- TensorFlow overview
- Keras
- Getting Started with TensorFlow
This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current “on-the-job” experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn how to:
Objectives
This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you’ll explore:
- Master Numerical Operations with NumPy: Gain proficiency in handling large numerical data sets, performing array operations, and using vectorized computation for increased efficiency.
- Advanced Data Manipulation with Pandas: Acquire the ability to clean, filter, and manipulate complex data sets using Pandas, allowing for more insightful data analysis.
- Implementing Scientific Computing with SciPy: Learn to apply complex mathematical functions and algorithms in Python using SciPy, thereby broadening your toolbox for scientific computing tasks.
- Visualizing Data Using Matplotlib and Seaborn: Develop advanced data visualization skills for creating comprehensive, interpretable visual representations of complex data sets.
- Data Merging and Concatenation Techniques: Understand and implement advanced techniques to merge, join, and concatenate data sets effectively, enabling better data integrity and usefulness.
- Utilizing Pillow for Image Processing: Become proficient in basic image processing tasks like loading, transforming, and saving images using the Pillow library, thus widening the range of data types you can manipulate.
- Applying Machine Learning Models with scikit-learn: Understand the fundamentals of machine learning algorithms and how to implement them using scikit-learn for tasks such as classification, regression, and clustering.
- Developing Problem-Solving Skills for Real-world Applications: Cultivate the ability to apply the acquired technical skills to solve real-world problems, enhancing your capacity to contribute effectively to data-driven projects in a professional setting.
If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goal
This course is geared for experienced data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics or eventual machine learning tasks. Attending students are required to have a background in basic Python for data science.
Students should have incoming practical skills aligned with those in the course(s) below, or should have attended the following course(s) as a pre-requisite:
- Fast Track to Python for Data Science and Machine Learning (3 days)
- Applied Python for Data Science & Engineering
Question: What if I have to reschedule my class due to conflict?
Answer: Ten (10) business days’ notice is required to reschedule a class with no additional fees. Notify TOPTALENT LEARNING as soon as possible at 469-721-6100 or by written notification to info@toptalentlearning.com to avoid rescheduling penalties.
Question: How do I enroll for this class?
Answer: Please contact our team at 469-721-6100; we will gladly guide you through the online purchasing process.
Question: What happens once I purchase a class?
Answer: You will receive a receipt and an enrollment confirmation sent to the email you submitted at purchase. Your enrollment email will have instructions on how to access the class. Any additional questions our team is here to support you. Please call us at 469-721-6100.
Question: What is your late policy?
Answer: If a student is 15 minutes late, they risk losing their seat to a standby student. If a student is 30 minutes late or more, they will need to reschedule. A no-show fee will apply. Retakes are enrolled on a stand-by basis. The student must supply previously issued courseware. Additional fees may apply.
Question: What happens when I finish my class?
Answer: You will receive a ‘Certificate of Completion’ once you complete the class. If you purchased an exam voucher for the class, a team member from TOPTALENT LEARNING will reach out to discuss your readiness for the voucher and make arrangements to send it.
