Data Science Projects with Python

KHDA accredited course

24-hour training led by industry professionals

6 course modules based on advanced Python applications

Lucrative career opportunities with our certification

Pay fees with easy instalments

4.85/5

6293 Enrolled

Overview

What our training includes

Master Data Preprocessing & Feature Engineering techniques for preparing real-world data for machine learning
Learn to use a leading Python library to build and assess machine learning models effectively
Gain a deep understanding of logistic regression, decision trees, and random forests
Master in optimising machine learning performance to achieve robust and generalizable models
Develop a data science workflow that integrates data preparation, modelling and client presentation

Learning Objectives

Upon finishing the course, you will be able to:

Comprehend the practical application of Python tools for data science projects

Describe Python libraries like Pandas and Matplotlib to explore and visualize data

Understand the methods involved in extracting and transforming features from raw data to create new variables

Get introduced to Scikit and learn how to use it to build and evaluate classification and regression models

Know how to use Scikit-learn to perform clustering and dimensionality reduction

Understand decision trees and their significance in data exploration

Ready to get started?

Overall ratings by our students

Upcoming sessions

Our Trainers

We, at Learners Point, take immense pride in our teaching methods and instructors. Our instructors are some of the best experts in their fields and employ a practical approach to learning. Many of them are globally recognised and have a diverse set of experience in their field of expertise. You are always sure to have the best in the industry as your teachers who are ready to guide you at every step and make the experience informative yet enjoyable. Apart from the focus on learning your chosen course, our instructors also encourage students to develop communication skills and interpersonal skills necessary to excel in the practical world.

Our cutting edge teaching methods make every program an immersive and productive experience for the learners. Our learning methods are research-driven and are continuously updated to stay relevant to present times as well as the future. You will enjoy practical applications of everything learned through theory and regular mock examinations to help monitor your progress. Our courses are led by an instructor in a classroom setup and we do offer online high-quality sessions as well for individuals. We also monitor the training sessions with a progress tracker to maintain high standards of instruction & ethics.

Related courses

Curriculum

There are no prerequisites required

Python and the Anaconda Package Management System
Different Types of Data Science Problems
Loading the Case Study Data with Jupyter and pandas
Data Quality Assurance and Exploration
Exploring the Financial History Features in the Dataset
Activity 1- Exploring Remaining Financial Features in the Dataset

Introduction
Model Performance Metrics for Binary Classification
Activity 2- Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve

Introduction
Examining the Relationships between Features and the Response
Univariate Feature Selection- What It Does and Doesn't Do
Building Cloud-Native Applications
Activity 3- Fitting a Logistic Regression Model and Directly Using the Coefficients

Introduction
Estimating the Coefficients and Intercepts of Logistic Regression
Cross Validation- Choosing the Regularization Parameter and Other Hyperparameters
Activity 4- Cross-Validation and Feature Engineering with the Case Study Data

Introduction
Decision trees
Random Forests- Ensembles of Decision Trees
Activity 5- Cross-Validation Grid Search with Random Forest

Introduction
Review of Modeling Results
Dealing with Missing Data- Imputation Strategies
Activity 6- Deriving Financial Insights
Final Thoughts on Delivering the Predictive Model to the Client

Frequently asked questions

With the explosion of data in recent years, companies are seeking skilled professionals who can help them analyze and interpret data for the growth of businesses. As a career path which offers a combination of high growth, good salary, a variety of job roles and opportunity to make an impact, data science is an attractive choice for many people today.

Dubai has a rapidly growing technology sector, with several multinational companies setting up their regional headquarters in the city. This has led to an increasing demand for data science professionals who can help these companies leverage data to drive growth and make informed business decisions.

Today, there is a growing demand for data science professionals across various industries. Building practical skills in data science can help you attract more opportunities by making you a versatile, experienced and valuable candidate with relevant skills and knowledge in a growing field.

Attending the Data Science Projects with Python course can provide you with a wide range of benefits, including:

Hands-on learning
Industry-relevant curriculum
Industry expert trainers

Our Data Science Projects with Python course provides participants with the foundational knowledge and practical skills required to succeed in the field of data science. It is ideal for a wide range of individuals and professionals like:

Aspiring data scientists
IT professionals
Researchers and academics
Anyone interested in data science

The training sessions at Learners Point Academy are interactive, immersive and an intensive hands-on programme. We offer 3 modes of delivery and participants can choose from instructor-led classroom-based group coaching, one-on-one training sessions and high-quality live and interactive online sessions at their convenience.

At Learners Point Academy, if a participant doesn’t wish to proceed with the training after registration for any reason, he or she is entitled to a 100% refund. However, the refund will be issued only if we are notified in writing within 2 days from the date of registration. The refund will be processed within 4 weeks from the date of withdrawal.