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Python for Data Analytics

Python for Data Analytics

12 Weeks

All levels

7 lessons

122 students

Program Overview

In the digital economy, being able to make decisions informed by data is not optional – it is essential. Organizations that make decisions based on data simply perform better.

The increasing importance of data analytics encouraged us to introduce a program that trains the learners in Phyton for data analytics with a statistical, computational and programming focus. This premium Python for Data Analytics course is an opportunity for graduates and working professionals from varied disciplines to develop advanced data analytics skills. At Edubex, our goal is to help every individual develop into a dynamic, skilled data scientist, adept at working in a variety of settings and capable of meeting the challenges of the industry globally.

During the program, learners will get an in-depth understanding of Data Analytics, Python for Data Analytics, NumPy Basics, Pandas data structure, Data aggregation, Data cleansing, Data Wrangling, Data Visualization, Design of experiments and ANOVA Machine learning and more. Our subject-matter experts have curated the course curriculum with the industry requirements in mind. By the end of this course, we will have a batch of competent, socially responsible and continuously employable professionals.

Starts On

06 April 2025

Duration

3 Months

Fees

£ 2450

Programme Modules

Module I: Introduction to Data Analytics & Python Environment

  1. Data Science Introduction
  2. Data Science Environments- Introduction to Python
  3. Anaconda Python Distribution and IPython Basics
  4. Built-in-Structures, Functions and Files in Python
  5. NumPy Basics and Introduction to Linear Algebra

Module II: Data Extraction and Summarization

  1. Python Pandas Basics and its Data Structures
  2. Pandas – Reading and Writing Data
  3. Web Scrapping
  4. Data summarization with descriptive statistics using pandas

Module III: Probability & Probability Distribution

  1. The concepts of probability
  2. Conditional probability and independence
  3. Radom variables and probability distribution
  4. Discrete & Continuous Probability Distributions

Module IV: Data Preparation

  1. Handling missing and redundant data
  2. Data transformation and discretization
  3. Data Wrangling
  4. Data Aggregation

Module V: Data Visualization and Sampling Distribution

  1. Data Visualization with Matplotlib
  2. Data Visualization with seaborn
  3. Central limit theorem
  4. Sampling distribution: chi-square, t and F-distributions

Module VI: Inferential Statistics

  1. The point estimation and confidence interval
  2. Inferential Statistics and Hypothesis Testing, Python stats-model package
  3. Test of significance (small sample test): t-test, F-test,x2-test of goodness of fit
  4. Statistical modelling with Pasty and stats-models
  5. Design of experiments and ANOVA
  6. Time series forecasting

Module VII: Data Modelling with Machine Learning

  1. Introduction to Machine learning with scikit-learn
  2. Supervised and Unsupervised machine learning
  3. Supervised Learning: Naïve Bayes and k-NN
  4. Supervised Learning: SVM and SVR
  5. Regression Methods: Linear and Logistic Regressions, Regularization Techniques

Admission Process

Step 1: Complete the application form for the course.

Step 2: Our Admissions team will review and shortlist your profile.

Step 3: Pay the registration fee to secure your seat in the course.

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