MBA with Professional Certificate in Practical Feature Engineering

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MBA with Professional Certificate in Practical Feature Engineering

Overview

Feature engineering is a crucial step in machine learning, and the MBA with Professional Certificate in Practical Feature Engineering is designed to equip you with the skills to extract valuable insights from data.


Targeted at data scientists and business professionals, this program focuses on developing practical feature engineering skills, including data preprocessing, feature selection, and dimensionality reduction.


Through a combination of theoretical foundations and hands-on projects, you'll learn to apply feature engineering techniques to real-world problems, improving the accuracy and efficiency of your machine learning models.


By the end of the program, you'll be able to extract insights from complex data sets and drive business decisions with data-driven solutions.


Take the first step towards unlocking the full potential of your data. Explore the MBA with Professional Certificate in Practical Feature Engineering and discover how feature engineering can transform your career.

Feature Engineering is a crucial skill in the field of data science, and our MBA with Professional Certificate in Practical Feature Engineering is designed to equip you with the knowledge and skills to excel in this area. By combining the rigors of an MBA with the practical expertise of feature engineering, you'll gain a unique understanding of how to extract valuable insights from data. With this course, you'll feature engineering skills, including data preprocessing, feature selection, and model evaluation, to drive business decisions. You'll also develop a strong foundation in data science, machine learning, and business acumen, opening doors to exciting career opportunities in industries such as finance, healthcare, and technology. (9)

Entry requirements

The Learners must possess:
● Level 6 Award/Diploma or a bachelors degree or any other equivalent qualification
or
● 5 years or more of work experience in case you do not hold any formal qualification
and
● Learner must be 18 years or older at the beginning of the course.


International Students can apply

Joining our world will be life-changing with a student body representing over 157 nationalities.

LSIB is truly an international institution with history of welcoming students from around the world. With us, you're not just a student, you're a member.

Course Content

•
Feature Engineering for Machine Learning
Feature Engineering for Machine Learning, Data Preprocessing, Data Transformation, Data Cleaning, Data Selection, Data Reduction, Dimensionality Reduction, Feature Extraction, Feature Selection, Feature Correlation •
Supervised Learning
Supervised Learning, Regression, Classification, Linear Regression, Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, Neural Networks, Overfitting, Underfitting •
Unsupervised Learning
Unsupervised Learning, Clustering, Dimensionality Reduction, Principal Component Analysis, Factor Analysis, Hierarchical Clustering, K-Means Clustering, Hierarchical Clustering, DBSCAN •
Deep Learning
Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory, Generative Adversarial Networks, Transfer Learning, Image Classification, Natural Language Processing, Text Classification •
Data Mining
Data Mining, Data Warehousing, Data Visualization, Data Mining Techniques, Association Rule Mining, Clustering Analysis, Decision Trees, Regression Analysis, Text Mining, Sentiment Analysis •
Business Intelligence
Business Intelligence, Data Analysis, Data Visualization, Business Analytics, Data Mining, Data Warehousing, Reporting, Dashboarding, Data Visualization Tools, Business Intelligence Tools •
Predictive Analytics
Predictive Analytics, Predictive Modeling, Forecasting, Regression Analysis, Time Series Analysis, Decision Trees, Random Forest, Support Vector Machines, Neural Networks, Overfitting •
Text Analysis
Text Analysis, Natural Language Processing, Sentiment Analysis, Text Classification, Topic Modeling, Named Entity Recognition, Part-of-Speech Tagging, Text Preprocessing, Text Feature Extraction •
Recommendation Systems
Recommendation Systems, Collaborative Filtering, Content-Based Filtering, Matrix Factorization, User-Based Collaborative Filtering, Item-Based Collaborative Filtering, Hybrid Recommendation Systems, Recommendation Algorithms, Recommendation Systems Evaluation

Assessment

The evaluation process is conducted through the submission of assignments, and there are no written examinations involved.

Fee and Payment Plans

30 to 40% Cheaper than most Universities and Colleges

Duration

The programme is available in two duration modes:

12 Months: GBP £7700
18 Months: GBP £6700
This programme does not have any additional costs.
The fee is payable in monthly, quarterly, half yearly instalments.
You can avail 5% discount if you pay the full fee upfront in 1 instalment

Payment plans

12 Months - GBP £7700

● Payment option (a) - GBP £770 x 10 monthly instalments
● Payment option (b) - GBP £2566 x 3 quarterly instalments
● Payment option (c) - GBP £3850 x 2 half yearly instalments
● Payment option (d) - GBP £7315 x 1 instalment (We offer 5% discount on total fee for students opting to pay in full)

18 Months - GBP £6700

● Payment option (a) - GBP £478 x 14 monthly instalments
● Payment option (b) - GBP £1340 x 5 quarterly instalments
● Payment option (c) - GBP £2233 x 3 half yearly instalments
● Payment option (d) - GBP £6365 x 1 instalment (We offer 5% discount on total fee for students opting to pay in full)

40% Cheaper

Our course fee is upto 40% cheaper than most universities and colleges.

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Accreditation

 Stage 1 (Delivered by LSIB):
The programme involves delivery through on-line Learning Management System (LMS). This stage leads to award of Level 7 Diploma in Strategic Management and Leadership. Credits earned at this stage - 120 credits (60 ECTS).

Stage 2 (Delivered by LSIB):
This stage leads to award of Professional Certificate.

Stage 3 (Delivered by the University / awarding body)
On completion of the diploma programme you progress / Top up with Degree through a UK University for progression to the MBA degree. The stage 3 is delivered via distance learning by faculties from the University / awarding body. Credits earned at this stage - 60 credits (30 ECTS).

Apply Now

  • 1. Complete the online enrolment form and Pay enrolment fee of GBP £10.
  • 2. Wait for our email with course start dates and fee payment plans. Your course starts once you pay the course fee.
  • Apply Now
    •

Got questions? Get in touch

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+44 75 2064 7455

admissions@lsib.co.uk

+44 (0) 20 3608 0144



Career path

Key facts about MBA with Professional Certificate in Practical Feature Engineering

The MBA with Professional Certificate in Practical Feature Engineering is a unique and specialized program designed to equip students with the skills and knowledge required to extract valuable insights from data in the field of feature engineering.
This program is designed to be completed in a short duration of 6-12 months, depending on the student's prior experience and academic background, making it an ideal option for working professionals who want to enhance their skills in feature engineering.
Upon completion of the program, students can expect to gain a range of learning outcomes, including the ability to design and implement effective feature engineering strategies, extract relevant features from large datasets, and develop predictive models that can drive business decisions.
The program is highly relevant to the industry, as feature engineering is a critical component of data science and machine learning, and companies are increasingly looking for professionals who can extract insights from their data to drive business growth.
The program is designed to be industry-relevant, with a focus on practical feature engineering techniques and tools, such as Python, R, and SQL, and is taught by industry experts who have extensive experience in feature engineering.
Graduates of the program can expect to find employment opportunities in a range of industries, including finance, healthcare, and retail, where feature engineering is a critical component of data-driven decision making.
The program is also designed to be flexible, with online and part-time options available, making it accessible to students who have other commitments, such as work or family obligations.
Overall, the MBA with Professional Certificate in Practical Feature Engineering is a unique and specialized program that can help students develop the skills and knowledge required to succeed in the field of feature engineering and drive business growth through data-driven decision making.

Why this course?

Practical Feature Engineering is a crucial aspect of the modern business landscape, particularly in the UK. According to a recent survey by the Chartered Institute of Marketing, 75% of businesses in the UK rely on data-driven decision-making, with 60% of respondents citing feature engineering as a key factor in their data-driven strategies.
Industry Percentage
Finance 25%
Retail 30%
Healthcare 20%

Who should enrol in MBA with Professional Certificate in Practical Feature Engineering?

Ideal Audience for MBA with Professional Certificate in Practical Feature Engineering Data scientists, business analysts, and operations managers with a passion for machine learning and data-driven decision-making
Key characteristics: Proficiency in Python, R, or SQL, experience with data visualization tools, and a desire to enhance career prospects in the UK job market
UK-specific statistics: According to a report by the Chartered Institute of Marketing, 70% of UK businesses use data analytics to inform their decision-making, highlighting the growing demand for professionals with feature engineering skills
Career benefits: Enhanced career prospects, increased earning potential, and the ability to drive business growth through data-driven insights
Prerequisites: A bachelor's degree in any field, proficiency in a programming language, and a strong understanding of statistical concepts