Data Science with Python Training in Singapore

SKU: 11029
12 Lesson
40 Hours
The most appropriate way to learn Python is to enroll in the best Python training institute in Singapore. With igmGuru, you will find yourself advancing as you understand the key constructs and concepts of Python. As you complete Python training in Singapore, you will find yourself with the ability to create your own Python programs and know the ML algorithms in this programming language. 

Data Science with Python Course in Singapore Overview

IgmGuru offers Data Science with Python Training in Singapore. Data Science with Python Course in Singapore has been designed after consulting with industry expert. The reason we have done this is because IgmGuru wanted to embed the topics and techniques which are practiced and are in demand in the industry – conduct them with the help of pedagogy which is followed across universities.In doing so, IgmGuru make our learners more industry/job-ready and this course is the gateway towards your Data Science career.

IgmGuru is one of the best Python Training Institute in Singapore which delivers a high level of quality training with the help of Industry Expert Professionals.

The demand and the supply gap for a data scientist is ever increasing. In fact, in one of its surveys, IBM predicts increment in data science jobs to be 364,000 to 2720,000 in 2020 which is only going upwards in the subsequent years. Python, as a programming language, is immensely popular for building data science-based applications owing to its simplicity, and large community support and ease of deployment.

IgmGuru's Data Science with Python Online Course in Singapore has been designed keeping in mind about learners who have zero to some level of exposure to Python. Any ideal session in this course would dedicate a good amount of time to understanding the theoretical part after which we will be moving on to the application of theoretical concepts by doing hands-on these statistical techniques. You would be provided with a lot of data set to practice things during the session and also to practice later on in the form of self-study which will help you in your journey of applied data science with python.

The three main pillars of Data Science with Python Training in Singapore.

  1. Application of mathematical and statistical concepts
  2. Expressing them using a programming language or a tool/platform
  3. Particular business domain

The Data Science with Python Training in Singapore modules focuses on explaining various use cases, some of the very famous applications/services which use Python, and then we gradually move to understand data science workflow using Python theoretically. We will help you understand the basic components of any data science model, right from fetching your data from your database to building a model that is in a deployable form.

What are the key deliverable's 

As you will progress in the Data Science with Python Online Training in Singapore, you will get to know the below things

  • Statistics for data science
  • Basic data cleaning techniques for model building
  • Converting your raw data into a machine consumable format
  • Working principle of machine learning models and their applicability
  • Understanding the parameters required for checking model accuracy
  • Deploying the model to make it available as a service
  • Maintaining the model over a period of time

With respect to the above steps, you will also learn how to use data science specific libraries in Python eg. Frequently used libraries in data cleaning like NumPy, pandas, spicy, groupby, merge; data plotting libraries like matplotlib, seaborn; machine learning-based modules available inside scikit learn for building various regression and classification based algorithms, libraries to check model accuracy like confusion matrix, MSE, RMSE, Natural Language-based libraries like NLTK, genism, VADER. 

A good amount of content has also been dedicated to Natural Language Processing techniques and various web scraping methodologies. Of late, NLP is gaining a lot of popularity owing to use in our day to day life eg. Mails, tweets, FB posts, WhatsApp chats are ideal input for any NLP based models. You are very likely to experience NLP based openings which is now-a-days considered to be a speciality within the Machine Learning branch. 

Hence assessing the market based demands, we have specifically designed modules to upskill you in this area as well – mostly its applied data science with python A very significant model in the area of NLP is Sentiment Analysis which is something we will be building to start things of and will move on to build much complex algorithm in this area.

Key Features

Data Science With Python Training in Singapore Modules

1. Data Science
2. Data Scientists
3. Examples of Data Science
4. Python for Data Science

1. Introduction to Data Visualization
2. Processes in Data Science
3. Data Wrangling, Data Exploration, and Model Selection
4. Exploratory Data Analysis or EDA
5. Data Visualization
6. Plotting
7. Hypothesis Building and Testing

1. Introduction to Statistics
2. Statistical and Non-Statistical Analysis
3. Some Common Terms Used in Statistics
4. Data Distribution: Central Tendency, Percentiles, Dispersion
5. Histogram, Bell Curve, Hypothesis Testing
6. Chi-Square Test, Correlation Matrix, Inferential Statistics

1. Introduction to Anaconda
2. Installation of Anaconda Python Distribution - For Windows, Mac OS, and Linux
3. Jupyter Notebook Installation, Jupyter Notebook Introduction
4. Variable Assignment
5. Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
6. Creating, accessing, and slicing tuples
7. Creating, accessing, and slicing lists
8. Creating, viewing, accessing, and modifying dicts
9. Creating and using operations on sets
10. Basic Operators: 'in', '+', '*', Functions, Control Flow

1. NumPy Overview
2. Properties, Purpose, and Types of ndarray
3. Class and Attributes of ndarray Object
4. Basic Operations: Concept and Examples
5. Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
6. Copy and Views, Universal Functions (ufunc)
7. Shape Manipulation, Broadcasting, Linear Algebra

1. SciPy and its Characteristics, SciPy sub-packages
2. SciPy sub-packages –Integration, SciPy sub-packages – Optimize
3. Linear Algebra
4. SciPy sub-packages – Statistics, SciPy sub-packages – Weave
5. SciPy sub-packages - I O

1. Introduction to Pandas
2. Data Structures, Series, DataFrame, Missing Values
3. Data Operations, Data Standardization
4. Pandas File Read and Write Support
5. SQL Operation

1. Introduction to Machine Learning
2. Machine Learning Approach
3. How Supervised and Unsupervised Learning Models Work
4. Scikit-Learn
5. Supervised Learning Models - Linear Regression, Logistic Regression
6. K Nearest Neighbors (K-NN) Model
7. Unsupervised Learning Models: Clustering, Dimensionality Reduction
8. Pipeline, Model Persistence, Model Evaluation - Metric Functions

1. NLP Overview
2. NLP Approach for Text Data
3. NLP Environment Setup
4. NLP Sentence analysis, NLP Applications
5. Major NLP Libraries, Scikit-Learn Approach
6. Scikit - Learn Approach Built - in Modules, Scikit - Learn Approach Feature Extraction
7. Bag of Words, Extraction Considerations
8. Scikit - Learn Approach Model Training
9. Scikit - Learn Grid Search and Multiple Parameters
10. Pipeline

1. Introduction to Data Visualization
2. Python Libraries, Plots
3. Matplotlib Features: Line Properties Plot with (x, y), Controlling Line Patterns and Colors, Set Axis, Labels, and Legend Properties, Alpha and Annotation, Multiple Plots, Subplots
4. Types of Plots and Seaborn

1. Web Scraping
2. Common Data/Page Formats on The Web
3. The Parser, Importance of Objects
4. Understanding the Tree, Searching the Tree
5. Navigating options, Modifying the Tree
6. Parsing Only Part of the Document
7. Printing and Formatting, Encoding

1. Need for Integrating Python with Hadoop
2. Big Data Hadoop Architecture
3. MapReduce, Cloudera QuickStart VM Set Up
4. Apache Spark
5. Resilient Distributed Systems (RDD)
6. PySpark, Spark Tools
7. PySpark Integration with Jupyter Notebook

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Course Fees

Online Class Room Program

US $ 599.00
Refund Policy
  • Duration : 40 hrs
  • Lifetime Free Upgrade
  • Reference Documents
  • 24x7 Support & Access

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  • Customized Training Delivery Model
  • Flexible Training Schedule Options
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Python Certification in Singapore

Once you have completed Data Science with Python Course in Singapore, you will have to go through  data science with python certification examination. This will mainly focus on the data science with python modules which learners have gone through. The certification exam will mainly consist of quizzes, MCQs and a project which you will have to build from scratch and submit. The project work would be graded after which the certification would be awarded.

Python Certification in Singapore


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