Data Science with Python

✓ Learning python for data science 
Prediction , Manipulating Data and Data Analysis
✓ Learning and implementing various Machine Learning Algorithms
✓ Validation of data
✓ Supervised and Unsupervised Learning
✓ Statistics

Data Science with Python

Course Price

RM 4,500

RM 3,000

(Duration: 15 days/60 hours)

Course Preview

Sign up for Python Fundamentals Course for $10

Overview

Get ready to exploit large datasets for insightful analysis and data-based prediction. In this training, you will master the technique of how Python is deployed for Data Science, working with Pandas library for Data Science, data cleaning, data visualization, Machine Learning, advanced numeric analysis, and many more. In addition, you'll experience building real-world-like projects that could help your resume stand out amongst your peers. Our Data Science with Python training course is an entire circle course that covers all aspects of data analysis processes and data science.

Highlights

  • 40 Hours of Live, Interactive, Trainer-Led Training 

  • Assignments  and Quizzes

  • 10 Hours Self Paced learning with Python with trainer support

  • 10 hours of SQL self paced learning

  • 10 hours power bi self paced learning

  • 5 hours excel self paced learning

  • 10 hours of Tableau self  paced learning

  • 5 hours of statistical essentials self paced learning

  • 2 projects , including Credit Card Fraud Detection analysis and Bank Churn Prediction using popular classification algorithms

  • 1 capstone project ; building model for prediction analysis

  • Science, data cleaning, data visualization, Machine Learning, advanced numeric analysis, etc.

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  • How to wrangle data, or Data wrangling

  • Learning to explore data or Data exploration

  • Visualising data

  • Learning how to scrap data from various sources or datasets

  • Fundamentals of Python programming 

  • Data Science libraries 

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  • Software Professionals

  • IT Professionals

  • Analytics professionals

  • Data Scientist

  • Data Analyst

  • Fresh Graduates

  • Anyone with a genuine interest in Data Science

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  • We aim is to provide everyone with vital hands-on experience so that you are well-prepared for job interviews alongside an exhibition of their positions.

  • Learn from pioneers in Data Science, both in research and industry.

  • Learn the tricks of the trade from seasoned Python Developer practitioners.

  • Work on hands-on projects that develop your ability to solve real-world problems.

  • Practice your skills on our hands-on projects that simulate real-world problems

  • How to wrangle data

  • Learning to explore data

  • Visualizing data

  • Learning how data is scrapped from various datasets or sources

  • Fundamentals of Python programming

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  1. Ability to use data with operators and functions

  2. Ability to access, index, and slice strings and other data

  3. Using various data structures in different contexts

  4. Implement decision making and flow control

  5. Understand the functionality of functions and modules 

  6. Extracting relevant data

  7. Utilise Pandas and DataFrames to organise & data filteration

  8. Ability to produce statistical inferences using Pandas and NumPy

  9. Ability to utilise NumPy for numerical and mathematical computations

  10. Expose to various analytics techniques with Pandas

  11. Data Visualization with matplotlib and seaborn

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  • 10 hours of python fundamentals self paced learning tutorial video

  • 4 hours of self paced learning on statistical essentials

  • 4 hours SQL fundamentals self paced learning tutorial video

  • Comprehensive Blended Learning program

  • flexible access to online classes

  • instructions carried out through industry experienced trainers

  • Interactive Quizzes

  • 15+ in-demand technologies and skills

  • Get hands-on experience with four industry-related projects

  • 24x7 learner assistance and support

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The inquiry process comprises three simple steps.

STEP 1 Submit Inquiry- Tell us a bit about yourself and the questions you want to enquire

STEP 2 Reviewing–Your questions will be processed and answered within a day or two 

STEP 3 Response–Answers will typically be sent through email. However, you may tell us the option you prefer us to contact you in

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  • Physical Classroom Training (Malaysia)

  • On-site Company Training (Malaysia)

  • Online Training via Microsoft Team (Malaysia and International)

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Training Fee : RM3000 (upon 60% discount)

Duration: 15 days/60 hours

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Module 1 – Introduction To Data Science And Data Science Libraries

Data science is the field of applying advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning and other uses. Through this module, you will learn the basics, how to analyze data, and then create some beautiful visualizations using Python.

Numpy

It’s a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. NumPy also addresses the slowness problem partly by providing these multidimensional arrays as well as providing functions and operators that operate efficiently on these arrays.

 

  • NumPy Getting Started

  • NumPy Creating Arrays

  • NumPy Array Indexing

  • NumPy Array Slicing

  • NumPy Data Types

  • NumPy Copy vs View

  • NumPy Array Shape

  • NumPy Array Reshape

  • NumPy Array Iterating

  • NumPy Array Join

  • NumPy Array Split

  • NumPy Array Search

  • NumPy Array Sort

  • NumPy Array Filter

  • NumPy Random

  • NumPy Inbuilt Methods


Module 2 – Pandas

Pandas is an important library in Python for Data Science. It is used for data manipulation and analysis.  It is well suited for different data such as tabular, ordered and unordered time series, matrix data, etc.

 

  • Pandas Getting Started

  • Pandas Series

  • Pandas DataFrames

  • Pandas Read CSV

  • Pandas Read JSON

  • Pandas Read Excel

  • Pandas Analyzing Data


Module 3 – Data Cleaning And Data Wrangling Using Python Pandas

Data scientists spend a large amount of their time cleaning datasets and getting them down to a form with which they can work. In fact, a lot of data scientists argue that the initial steps of obtaining and cleaning data constitute 80% of the job. Therefore, if you are just stepping into this field or planning to step into this field, it is important to be able to deal with messy data, whether that means missing values, inconsistent formatting, malformed records, or nonsensical outliers. In this module, we’ll leverage Python’s Pandas to clean data.

 

  • Cleaning a DataFrame

  • Removing Columns

  • Removing Rows

  • Filling Missing Values

  • Improving Readability

  • Dropping Columns in a DataFrame

  • Changing the Index of a DataFrame

 

Module 4 – Matplotlib Visualization with Python

Matplotlib is a python library used to create 2D graphs and plots by using python scripts. It has a module named pyplot which makes things easy for plotting by providing feature to control line styles, font properties, formatting axes etc. It supports a very wide variety of graphs and plots namely - histogram, bar charts, power spectra, error charts etc

 

  • Python Data Visualization

  • Python Chart Properties

  • Python Chart Styling

  • Python Box Plots

  • Python Heat Maps

  • Python Scatter Plots

  • Python Line Charts

  • Python Pie Charts

  • Python Bar Charts

  • Python Time Series

  • Python Geographical Data


Module 5 – Python seaborn Library

Seaborn is one of an amazing library for visualization of the graphical statistical plotting in Python. Seaborn provides many color palettes and defaults beautiful styles to make the creation of many statistical plots in Python more attractive. Seaborn library aims to make a more attractive visualization of the central part of understanding and exploring data. It is built on the core of the matplotlib library and also provides dataset-oriented APIs.

 

  • Plotting Chart Using seaborn Library

  • Line plot

  • Dist plot

  • Lmplot

  • Histogram

  • Bar Plot

  • Count Plot

  • Point Plot

  • Violin Plot

  • Heatmap

 

Module 6 –  Statistics

  • What is statistics?

  • Basic terminology of statistics

  • Types of statistics

  • Descriptive statistics

  • Measure of Central Tendency ( Mean, median, mode )

  • Measures of Dispersion ( Variance, Standard Deviation, Range-its derivation )

  • Inferential statistics

Module 6 – Exploratory Data Analysis

In this module, you will learn what is meant by exploratory data analysis, and you will learn how to perform computations on the data to calculate basic descriptive statistical information, such as mean, median, mode, and quartile values, and use that information to better understand the distribution of the data. You will learn about putting your data into groups to help you visualize the data better. Exploratory data analysis (EDA) is an especially important activity in the routine of a data analyst or scientist. It enables an in depth understanding of the dataset, define or discard hypotheses and create predictive models on a solid basis. It uses data manipulation techniques and several statistical tools to describe and understand the relationship between variables and how these can impact business.

 

Capstone Project 1 : Credit Card Fraud Detection Case Study

Overview : Lots of financial losses are caused every year due to credit card fraud transactions, the financial industry has switched from a posterior investigation approach to an a priori predictive approach with the design of fraud detection algorithms to warn and help fraud investigators.

This case study is focused to give you an idea of applying Exploratory Data Analysis (EDA) in a real business scenario. In this case study, apart from applying the various Exploratory Data Analysis (EDA) techniques, you will also develop a basic understanding of risk analytics and understand how data can be utilized in order to minimize the risk of losing money while lending to customers.

 

Module 7 - Introduction To Machine Learning

  • Introduction To Machine Learning

  • Types Of Machine Learning

 
Module 8 - Supervised Learning - Regression 

  • Linear Regression

  • Programming of Linear Regression in Python-scikit learn
     

Capstone Project 2 - Predicting Student Grades using simple linear regression
Overview : The aim here is to predict a students final score based on a number of Hours studied. This will be a regression based ML project using sklearn.

  • Polynomial Regression

 

Capstone Project 3 - Predict Profit using Multiple Linear Regression
Overview : In this you will be designing a python project that will predict future profit made by the start ups. In this you will be learning multivariate regression.
 
Module 9 -  Supervised Learning - Classification

  • Logistic Regression

 

Capstone Project 4 - Healthcare Industry Predicting Diabetes
This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. In this you will be learning the concepts of Logistic Regression.
 
Module 9 -   Unsupervised Learning

  • Types of Unsupervised Learning

  • Applications of Unsupervised Learning

  • Introduction to Clustering Algorithms

  • Types of Clustering Algorithms

  • What is K-Means Clustering?

  • Implementation Of Apriori Algorithms

Capstone Project 5 - Apriori Algorithm is a Machine Learning algorithm utilized to understand the patterns of relationships among the various products involved. The most prominent practical application of the algorithm is to recommend products based on the products already present in the user’s cart. Walmart especially has made great use of the algorithm in suggesting products to it’s users.

Module 10 - Apply different Machine Learning algorithms


Capstone Project 6 - Bank Churn Prediction using popular classification algorithms
Knowing the customer churn rate is a key indicator for any business. According to a study by Bain & Company, improving the customer retention rate for existing customers by just 5 percent can improve a company’s profitability by 25 to 95 percent.

  •  In this module, we are going to look at the following:

  • Initial Exploratory Data Analysis

  • Predicting the churn rate for a customer and classify them by learning about different classification algorithms.

  • Comparing and evaluating different algorithms based on its performance.

  • And once we have our best model, we would perform optimization

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FAQ for Data Analytics 

What is Nexperts Academy’s Certified Data Analytics Associate with Python course about ?

This course is designed by industry experts preparing you to be certified as a Data Analytics Associate by a globally recognised accreditation body, Python Institute and also transforms you into an expert Data Analytics professional. You will be mastering all data analysis process and acquiring new skills such Tableau, Python, Excel, Machine Learning, Python Libraries , Power BI. 

Why should I enroll for hybrid Certified Data Analytics Associate with Python course?

Our course doesnt only give the students the necessary skills and knowledge on data analytics but also prepares you to become a renowned data scientist. Over and above that, Nexperts Academy has trained over 1000 fresh graduates in the year 2021 and 2022 and many of them have acquired good jobs as data analyst , digital marketers , junior programmers. We have also trained many corporates primarily from the banking and finance industry training their existing auditors , accountants, risk managers customised data analytics course , making our curriculum a solid and well structured syllabus incorporating latest industry best practices.

Are the Data Analytics Training Course Instructor Led or Self Paced?

It is combination of Live instructor led training that will held on a back to back basis and at the beginning of the training. You will then upon guidance of the trainer, you will be going through the self paced tutorials and would submit all assignments and projects respectively to the trainer.

How long will it take me to be a certified Data Analyst ?

Even upon taking the 40’hours live instructor led training , you can start preparing for the certification.

What are the topics that I should study orderly in this hybrid Certified Data Analytics Associate with Python course?

You will start with the 40 hours of Data Science topics and then move towards data visualisation and analysis with Excel, Tablaue , Power Bi and SQL self paced learning tutorial.

What kind of certification will I receive upon joining Nexperts Academy’s training?

You will receive certificate of completion and upon through preparation of the exam and passing of the exam, you will receive Certified Data Analyst Associate with Python certification.

Why should I consider beginning my career as a Data Analyst?

It is job role that is highly in demand but less in manpower. Not only the salary is higher but because there are less data analysts supply in the market it makes the salary demand much higher . On top of that, this job position can be acquired by any degree or diploma holders or working professionals in any field for as long as you have the problem solving and critical thinking skills.

What is programming knowledge necessary for this Data Analyst Certification Training?

Absolutely not . You will learn python programming basics during the training itself.

Is this Data Analyst Course recommended for beginners?

Yes

What are the prerequisites for Certified Data Analytics  Associate with Python Course?

There isnt any prerequisites for this course. Beginners with zero knowledge are welcome to join this training

What is the average salary of a Data Analyst in Malaysia?

As per Glassdoor 2022 survey the basic salary of a data analyst starts from RM4,000 per month

WHY NEXPERTS ACADEMY