Ferdsilinks Academy is your ultimate destination for learning data skills that can transform your career and business. We offer practical and hands-on training programs and courses in data analysis, data science, digital marketing, web development, research methodology, graphic design and more.
Our courses are designed with industry professionals and follow a 90% practice, 10% theory approach. You will learn how to use data to solve real-world problems in various domains and industries.
Our vision is to close the skill gap created by the educational system and provide you with the right skills for the global market.
Our mission is to prepare you for employment opportunities across all industries by equipping you with data-related skills that are in high demand.
Don’t just take our word for it. Hear what our happy students have to say about us:
[Ndong Nelson Peng
Alumni
Data Analysis Training Program by Ferdsilinks was a dream come true for me. I learned a lot about data analysis without any barrier and gained skills and a career that opened new doors for me. I became confident as a data analyst even before getting certified. I am grateful to Ferdsilinks Foundation for this opportunity. Thanks to them, I am now excelling in Research Assistance and Data Analysis. They always support me when I face any difficulty.]
Ready to join our community of data enthusiasts? Contact us today at ferdsilinks@gmail.com or visit our website at www.ferdsilinks.com. We look forward to helping you achieve your goals!
- Courses
- Data Science with python
- Data Science with Python
- DSP101
- Introduction
- After statistics essentials, this course acquaints students to data analysis with the Python (most popular data science language today). You learn to work with different data structures in Python using the most popular data analytics and visualization packages such as numpy, pandas, matplotlib, and seaborn. Ultimately, students will use Python code and packages to solve problems; extract, transform, load, and analyze data to gain insights; and communicate the analyses, aided by appropriate visualizations. The course targets students mainly beginners who want to learn the basics of data analysis with python, or intermediate learners who want to improve their skills and apply them to real-world problems.
- Objectives and Outcome
- The objectives of this course are to help you learn how to collect, clean, manipulate, analyze, and visualize data using python, and to use various python libraries and tools for data science, such as pandas, numpy, matplotlib, scikit-learn, and more. https://www.slideshare.net/TingomFerdinand2/data-science-with-python-course-outlinepptx
- The Prerequisites
- To be successful in this course, basic programming knowledge is necessary. However, this course do not assume any previous knowledge in python programming, such as data types, variables, operators, functions, loops, and conditions, and to have a basic understanding of data analysis concepts, such as statistics, probability, and machine learning.
- Detailed Course Outline
- Introduction to data analysis with python
- What is data analysis and why use python?
- Setting up the python environment and tools
- Importing and exporting data with python
- Data manipulation with pandas
- What is pandas and how to use it?
- Creating and exploring data frames
- Filtering, sorting, and grouping data
- Merging, joining, and concatenating data
- Putting it all together with real world data/Portfolio
- Data visualization with matplotlib
- What is matplotlib and how to use it?
- Creating and customizing plots
- Choosing the right plot for your data
- Adding labels, legends, and annotations
- Putting it all together with real world data/Portfolio
- Data analysis with numpy
- What is numpy and how to use it?
- Creating and manipulating arrays
- Performing arithmetic and logical operations
- Applying statistical and mathematical functions
- Machine Learning
- Concepts in Machine learning
- Data analysis with scikit-learn
- What is scikit-learn and how to use it?
- Preprocessing and transforming data
- Splitting and cross-validating data
- Evaluating and comparing model
