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Title: DATA ANALYTICS AND STATISTICAL MODELLING. Objective: At the end of this course, learners will be able to:  Use Python and Pandas to collect data from different sources Merge data using Pandas  Check and clean for the 5 common data issues: outliers, duplicates, missing values, out-of-range values and inconsistencies. State the 5 data quality metrics: accuracy, completeness, auditability, consistency, validity. Create databases in PostgreSQL Create tables in PostgreSQL with multiple columns and different data types including integers, floats, text and dates Create functions in PostgreSQL to perform data processing Create a data dictionary based on a set of guidelines for a given dataset.  Document ETL processes including: data source, data destination, ER Diagrams, description of transformation.  Perform 5 checks for structure and quality of data warehouse. Description: This course will introduce learners to the steps of the Data Analytics workflow of Extract, and Wrangle/Prepare. Learners will enhance their growing skills in data acquisition, analysis, and visualization using Python programming fundamentals, data acquisition with APIs, exploratory data analysis, and simple linear regression. Area: Information and Communications. Training Provider: GENERAL ASSEMBLY SPACE ACADEMY PTE. LTD.
Title: (SCTP) Data Science Professional. Objective: Learners will be able to:  1. acquired a solid foundation in both Data Science and Probability/Statistics. 2. acquired proficient in data preparation and programming for data science in Python. 3. Work with big data, different data sources and databases usch as Hadoop and Spark, distributed computing with Hadoop and Spark, data warehousing and ETL, NoSQL databases like MongoDB and Cassandra, and stream processing with Kafka and Storm. This learning unit also covered the types of data sources and databases, data modelling and normalization, structured data sources and databases, relational databases, SQL queries, MySQL and PostgreSQL installation, configuration and usage, and data warehousing and OLAP.  4. Develop a foundation in the different Machine Learning techniques and be able to apply them in real-world scenarios. 5. acquired a solid understanding of the principles of data visualisation, and be able to create interactive and compelling data visualisations using different tools and techniques for effective communication of insights to stakeholders. 6. acquired the knowledge and application skills in problem solving, to identifying a real-life problem or an opportunity in their companies or businesses and to develop a solution or a plan to capture the opportunity. 7. apply knowledge to gain insights, know how to communicate effectively as a leader that is increasingly important to drive adaptability and change. Description: (SCTP) Data Science Professional is a multidisciplinary programme that combines aspects of computer science, mathematics, statistics, and engineering to extract knowledge and draw insights from large datasets.  It is a rapidly growing field with a wide range of applications in analytics, forecasting, optimization, machine learning, artificial intelligence (AI), natural language processing (NLP), and more.  In this course, students will be introduced to the process of data analysis from start to finish – from collecting data and refining it for analysis to communicating the results of their research. The goal is to equip them with an understanding of key data science principles and techniques as well as an appreciation for the challenges associated with real-world applications.  In addition to exploring fundamental concepts such as descriptive statistics and visualization methods, students will learn how to apply sophisticated algorithms and the Python programming language in order to bring powerful insights from complex datasets.  They will explore topics such as supervised learning models for predictive analysis (e.g., linear regression), unsupervised machine learning approaches for clustering or pattern recognition (e.g., K-means clustering), nonlinear models for nonparametric problems (e.g., decision trees), natural language processing techniques for text analytics (e.g., sentiment analysis), deep learning networks for image recognition/classification tasks (e.g., convolutional neural networks), reinforcement learning algorithms for autonomous robotic systems (e.g., Q-learning), time series analysis for forecasting methods such as ARIMA models or Holt-Winters smoothing techniques, and much more. Area: Information and Communications. Training Provider: BELLS INSTITUTE OF HIGHER LEARNING PTE. LTD.
Title: How to Critically Reason with Data on Microsoft Excel. Objective: "This course aims to equip the learner with both (1) the technical skills to carry out basic data preparation and analysis using Microsoft Excel, and more importantly, (2) the critical reasoning skills to conduct data analysis well. Specifically, how to think critically about data, how to critically evaluate interpretations of data, and how to formulate strong justifications using data.  Learning outcomes: Course participants are expected to be able to apply the following skills in a variety of circumstances at work: 1. Effectively use Microsoft Excel to manage large datasets  2. Analyse data to construct a narrative about what is going on with the dataset 3. Analyse data to identify potential root causes to known problems 4. Critically evaluate interpretations of data to identify flaws and develop sound justifications using data so as to strengthen the decision-making process". Description: """This course aims to equip the learner with both (1) the technical skills to carry out basic data preparation and analysis using Microsoft Excel, and more importantly, (2) the critical reasoning skills to conduct data analysis well. Specifically, how to think critically about data, how to critically evaluate interpretations of data, and how to formulate strong justifications using data.  Learning outcomes: Course participants are expected to be able to apply the following skills in a variety of circumstances at work: 1. Effectively use Microsoft Excel to manage large datasets  2. Analyse data to construct a narrative about what is going on with the dataset 3. Analyse data to identify potential root causes to known problems 4. Critically evaluate interpretations of data to identify flaws and develop sound justifications using data so as to strengthen the decision-making process""". Area: Information and Communications. Training Provider: NATIONAL UNIVERSITY OF SINGAPORE.
Title: Data Analytics. Objective: Understand the key concepts relating to the application of data analytics in business. - Understand and apply key statistical analysis concepts. - Import data into a spreadsheet and prepare it for analysis using data cleansing and filtering techniques. - Summarise data sets using pivot tables and pivot charts. - Understand and apply data visualization techniques and tools. - Create and share reports and dashboards in a data visualization tool. Description: Identify the main phases of data analysis: business understanding, data understanding, data preparation, modelling, evaluation, deployment. - Recognise data protection considerations when - Recognise data protection considerations when analysing data like: anonymise personal data if possible, comply with applicable data protection regulations. - Calculate the central tendency value of a data set using a function: mean, median, mode. - Calculate the variation of a data set: quartile, variance, range. - Import data into a spreadsheet application: .csv file, spreadsheet, website table, database table. - Validate that given values belong to a reference data set using the vlookup function. - Extract values from a string using text functions: left, right, len, mid, find. - Format a data set as a built-in table. - Change the method of aggregation for a value: sum, average, count, minimum, maximum. - Display multiple aggregation values. - Display values as: % calculation, difference from specific values, running total, ranked. - Automatically, manually group data and rename groups. - Ungroup data. - Insert and use slicers to filter single, multiple pivot tables. - Insert and filter a timeline. - Insert a pivot chart for an existing pivot table. - Create a pivot chart from fields in a data set. - Understand the concept of data visualization using reports and dashboards. Outline common visualizations like: charts, key performance indicators (KPIs), maps. - Recognise common data visualization tools and their functions like: visualise data, publish and share business intelligence. - Import a data set from a spreadsheet into a data visualization tool and save the file. - Create tables in a report. - Visualise data as a chart: column, bar, line, pie. - Apply, edit font and background conditional formatting to show: high/low values, above/below average values. - Apply, edit data bars. - Apply, edit visual leve. Area: Information and Communications. Training Provider: SINGAPORE NATIONAL EMPLOYERS FEDERATION.
Title: Business Analytics with SQL and Python. Objective: By attending this course, you will acquire foundational skills in SQL and Python to deliver powerful analysis and prediction for your team or business.   Develop the critical data wrangling, exploring, analysing and communication of data skills that will make you highly desirable in the industry.  This course is developed in collaboration with RMIT Australia. Description: Module 1 - Understand the data analytics process, define key performance metrics and the data needed to solve a business problem.  Module 2 - Gain the SQL skills by exploring various types of SQL (Structured Query Language) and create and insert data into a table, and develop an Entity Relationship (ER) model.  Module 3 - Cleaning and wrangling data, identifying data to clean and artefacts to drill into data analysis, how to connect Python to an SQL database and replicate how to extract SQL data to Python.  Module 4 - Introduce Panda and NumPy for data analysis and where to use them, utilise Panda in your dataset and adreess missing data analytics functions with NumPy.  Module 6 - Outline a foreacsting model and build a forecast model using Python, apply static, animated and interactive visualisation in Python to your dataset, and how to use Matplotlib to develop charts that can be used to present insights.  Module 6 - Final project that you need to set a business question, analyse and shape the data to create a data visualisation, and create and present your findings in a slide deck presentation to audience. Area: Information and Communications. Training Provider: SINGAPORE INSTITUTE OF MANAGEMENT GROUP LIMITED.
Title: Mining Data for Insights @ Work. Objective: This course will give participants a strong foundation in manipulating data in spreadsheets to support data analysis or analytics and will help participants to better position themselves for success within their organizations. Participants will acquire the necessary skills to execute basic analysis, visualize and communicate data with charts and graphs and ensure that they gather a perspective that will make them more productive and allow them to become a valuable asset to their organization in a digital age. Description: • Data preparation • Mathematical formulas • Mathematical and statistical functions • Relative versus absolute addressing • Pivot tables                                                                      • Grouping /Aggregations • Data visualization • Charts and graphs (Bar chart, Stacked bar chart, Histogram, Dual-axis chart, Pie chart, Area chart, Line chart/Trend line, Scatter plot. Bubble chart, Boxplot) Upon completion of this course, participants will be able to: • Prepare and analyse data in spreadsheets  • Identify appropriate mathematical processes necessary to achieve a result in a spreadsheet • Create mathematical formulas in a spreadsheet • Describe relative and absolute cell referencing • Devise formulas utilizing relative and absolute cell references in formulas • Explain mathematical and statistical functions and apply them in cell formulas • Explain the use of tables for managing and analysing a group of related data • Manage data through the use of tables • Determine appropriate use pivot tables to group and ungroup data • Create a pivot table in Excel • Classify explanatory and exploratory data visualization • Describe the various types of charts that can be used to present data visually • Choose appropriate chart styles to represent data • Use preview to view reports online. Area: Information and Communications. Training Provider: Republic Polytechnic.
Title: Data Wrangling. Objective: A. Knowledge and Understanding<BR> 1. Assess appropriateness of database designs based on characteristics of data and analytics needs<BR> 2. Critique data visualisations constructively<BR> B. Key Skills<BR> 1. Construct suitable SQL queries to acquire and reshape data<BR> 2. Assemble effective data flows in Tableau Prep as part of a reproducible workflow process<BR> 3. Create Python scripts to automate the acquisition and processing of web and spreadsheet data<BR> 4. Design and implement effective data visualisations in Tableau Desktop. Description: 1 Assess appropriateness of database designs based on characteristics of data and analytics needs<BR> 2 Critique data visualisations constructively<BR> 3 Construct suitable SQL queries to acquire and reshape data<BR> 4 Assemble effective data flows in Tableau Prep as part of a reproducible workflow process<BR> 5 Create Python scripts to automate the acquisition and processing of web and spreadsheet data<BR> 6 Design and implement effective data visualisations in Tableau Desktop. Area: Information and Communications. Training Provider: SINGAPORE UNIVERSITY OF SOCIAL SCIENCES.
Title: Data and Statistical Analytics 2.2. Objective: Upon completion of this course, learners will be able to support the collection of data required for data analytics application. Description: The purpose of this training is to give an introduction to the use of a spreadsheets tool like Excel to support the collection of data required for data analytics application. At the moment, many such businesses are attempting to convert manual paper-based processes to digital computer-based processes. However, the workforce may not have a deep understanding of the use of a common tool such as Excel and how to properly and systematically perform data scrubbing for computational interpretation.  This is a continuation of our first course on Data and Statistical Analysis (Level 1), in response to learners looking to progress to a higher level of data interpretation using Excel.  Performance Gaps / Needs that the Course Will Address: - Lack of understanding how to interpret data effectively using appropriate features and functions  - Lack of experience in using such data collection / storage software  - Lack of knowledge in data cleansing  Intended situation:  - Knowledge of data interpretation using appropriate features and functions  - Able to collect data for analysis, processing and improvement  - Able to clean data in Excel spreadsheets. Area: Accounting, Banking & Finance. Training Provider: AUSTON INSTITUTE OF MANAGEMENT PTE. LTD.
Title: Data Analytics and Visualisation Begins With Me (Synchronous  & Asynchronous). Objective: This course aims to provide participants with a foundation in: •Overview of data and data analytics •How to read, work, analyse and argue using data   •Data as a language and common terminologies  •Data analytics process and project life cycle  •Tools and technology used for data analytics and visualisation •Machine learning and data visualisation techniques   At the end of the course, participants will be able to: •Apply data analytics techniques using tools through hands-on sessions •Apply data visualisation techniques. Description: We live in a world that revolves around data. Data is regarded as the key resource that powers the world in the 21st century.  According to the International Data Corporation (IDC), many organisations still lack the data literacy skills to achieve business value.  Organisation that have accumulated a large amount of data would not see much benefits until it is harnessed to work for your organisation. Poor data literacy is one of the stumbling blocks and limits a company’s ability to reap value from data. To combat this, organisations will have to promote and develop their staff’s competencies in data.  Data literacy as defined by MIT is the ‘ability to read, work with, analyse and argue with data’. It is a set of core essential skills everyone in an organisation needs to use data effectively in their day-to-day activities and for decision-making. Used in the right way, data can help every employee achieve their objectives, perform their job better and contribute to overall company performance.  Employees who are data literate will also understand how to handle data appropriately, which will reduce the number of data breaches. Managers have to lead their organisations by setting the direction and the strategies on how data can be applied in the various parts of the organisation.   This course is designed for all professionals to gain the fundamental skills to understand and work with data for the organisation. This course aims to provide participants with a foundation in: •Overview of data and data analytics •How to read, work, analyse and argue using data   •Data as a language and common terminologies  •Data analytics process and project life cycle  •Tools and technology used for data analytics and visualisation •Machine learning and data visualisation techniques   At the end of the course, participants will be able to: •Apply data analytics techniques using tools through hands-on sessions •Apply data visualisation techniques. Area: Others. Training Provider: NATIONAL UNIVERSITY OF SINGAPORE.
Title: Analyse Business Data and Create Interactive Dashboards using Python (SF) (Synchronous e-learning). Objective: With the understanding and analyzing data as one of the key skills required in the industry today, this course is focused on the various aspects of data analytics and creating interactive dashboards using Python. Participants will be taught on the use of the key libraries for data ingestion and manipulation, exploratory data analysis, model building and data visualisation as well as the basic statistics knowledge required to understand the concepts of data analysis and visualisation. Description: MODULE 1: Understanding Data To learn how to get data from external sources into the Python environment and manipulate and analyze data. Before any data analytics project, it is very important to use statistical algorithms and methods to analyze data as part of the data analytics process. • Introduction to python packages for data manipulation  • Importing and exporting data • Importing datasets and understanding data  • Basics of analyzing the data MODULE 2: Data Wrangling To learn how python libraries can be leveraged to deal with data inconsistencies, issues with data and making them fit for data analytics. This is where 80% of today’s data scientists and engineers spend their time and it is very important to know how to do it. • Dealing with data issues and preparation in Python  • Data Formatting and conversions in Python • Data Wrangling  • Working with Pandas MODULE 3: Exploratory Data Analysis The objective of this module is to understand how to use appropriate statistical methods and visualizations for descriptive analytics. • Performing descriptive statistics with Python • Correlations, Scatter-plots and charts with matplotlib in Python  • Understanding data analysis with respect to various business scenarios MODULE 4: Model Development for Analysis This module gives the participants the very first steps towards forming hypothesis and doing predictive analytics. It aims to equip participants with the basics of how supervised machine learning models work and how evaluation and optimization can be carried out. • Hypothesis Testing • Linear Regression and Multiple Regression Models  • Model Evaluation Methods • Model selection MODULE 5: Data Visualization The objective of this module is to understand basic data metrics and KPIs (Key Performance Indicators) of different business use cases and plotting advanced interactive visualizations for data analysis to gain insights from the data. • Understanding basic metrics and. Area: Information and Communications. Training Provider: NTUC LEARNINGHUB PTE. LTD.
Title: Data Analytics and Visualization (Synchronous E-learning). Objective: Data analytics is used widely across virtually every industry, as data is becoming a key driver in business decisions. In fact, for a company to succeed, it will require the use of data driven decisions to ensure that the company is sure that it has the right information it requires to remain competitive. The course will focus on the important technical skills of data prepping, cleaning and manipulation in order to achieve the desired output to cater to the companys’/industry needs. Lesson Overview: LO1 – Understand the key elements of data analytics - 2 hours LO2 – Develop technical skills in python to process data - 5 hours LO3 – Develop competencies in python packages such as numpy and pandas to work with big data - 2 hours L04 – Apply techniques for data exploration and transformation - 2 hours LO5 – Apply visualization techniques to understand data better - 2 hours LO6 – Apply basic models to analyse data relationships and make predictions - 1 hours. Description: Data Analytics with Python is a comprehensive course that covers the principles of data analytics including visualization for data exploration. These principles include statistical review and then learners will be able to develop strong technical competency in processing data. The tools are mainly packages and functions in python that can aid in quick data processing and transformation of Big Data. Area: Information and Communications. Training Provider: VIBES GROUP PTE. LTD.
Title: Data Analytics and Visualization with Python. Objective: Expand your knowledge in data analytics and visualization with our WSQ-endorsed course focused on Python. Dive into key topics like data manipulation, statistical analysis, and visualization techniques using Python's powerful libraries. Through practical exercises and real-world examples, you'll learn to transform raw data into actionable insights and visual narratives that are compelling and easy to understand. By the end of this course, you'll be adept at leveraging Python for advanced data analytics and visualization tasks. Whether you're a data scientist, business analyst, or someone interested in generating insights from data, this course will provide you with the tools and skills to make impactful, data-driven decisions. Description: Learning Outcome: - prepare data for analysis - transform data for analysis - gain insights from data visualization - perform statistical data analysis and time series analysis - perform advanced data analysis to interpret results Course Outline: Topic 1 Data Preparation Data Analytics with Pandas Pandas DataFrame and Series Import and Export Data Filter and Slice Data Clean Data Topic 2 Data Transformation Join Data Transform Data Aggregate Data Topic 3 Data Visualization Data Visualization with Matplotlib and Seaborn Visualize Statistical Relationships with Scatter Plot Visualize Categorical Data with Bar Plot Visualize Correlation with Pair Plot and Heatmap Visualize Linear Relationships with Regression Topic 4 Data Analysis Statistical Data Analysis Time Series Analysis Topic 5 Advanced Data Analytics Data Piping Groupby and Apply Custom Functions Linear Regression. Area: Information and Communications. Training Provider: TERTIARY INFOTECH PTE. LTD.
Title: Analyse Business Data and Create Interactive Dashboards using Python (SF). Objective: With the understanding and analyzing data as one of the key skills required in the industry today, this course is focused on the various aspects of data analytics and creating interactive dashboards using Python. Participants will be taught on the use of the key libraries for data ingestion and manipulation, exploratory data analysis, model building and data visualisation as well as the basic statistics knowledge required to understand the concepts of data analysis and visualisation. Description: MODULE 1: Understanding Data To learn how to get data from external sources into the Python environment and manipulate and analyze data. Before any data analytics project, it is very important to use statistical algorithms and methods to analyze data as part of the data analytics process. • Introduction to python packages for data manipulation  • Importing and exporting data • Importing datasets and understanding data  • Basics of analyzing the data MODULE 2: Data Wrangling To learn how python libraries can be leveraged to deal with data inconsistencies, issues with data and making them fit for data analytics. This is where 80% of today’s data scientists and engineers spend their time and it is very important to know how to do it. • Dealing with data issues and preparation in Python  • Data Formatting and conversions in Python • Data Wrangling  • Working with Pandas MODULE 3: Exploratory Data Analysis The objective of this module is to understand how to use appropriate statistical methods and visualizations for descriptive analytics. • Performing descriptive statistics with Python • Correlations, Scatter-plots and charts with matplotlib in Python  • Understanding data analysis with respect to various business scenarios MODULE 4: Model Development for Analysis This module gives the participants the very first steps towards forming hypothesis and doing predictive analytics. It aims to equip participants with the basics of how supervised machine learning models work and how evaluation and optimization can be carried out. • Hypothesis Testing • Linear Regression and Multiple Regression Models  • Model Evaluation Methods • Model selection MODULE 5: Data Visualization The objective of this module is to understand basic data metrics and KPIs (Key Performance Indicators) of different business use cases and plotting advanced interactive visualizations for data analysis to gain insights from the data. • Understanding basic metrics and. Area: Information and Communications. Training Provider: NTUC LEARNINGHUB PTE. LTD.
Title: Analyze and Visualize Data With Python. Objective: This course teaches you how to analyse and visualize data using Python and interpret the results for business insights. You will learn to use Pandas, Matplotlib and Seaborn packages of Python for Statistical analysis and visualization of data. The key learning objectives are listed below: Learning Objectives: 1: Assess & plan data preparation based on the required analysis 2: Transform data for analysis 3: Visualize data using charts 4: Analyse using statistical techniques 5: Evaluate the results and interpret them for insights into business. Description: This course teaches you how to analyse and visualize data using Python and interpret the results for business insights. You will learn to use Pandas, Matplotlib and Seaborn packages of Python for Statistical analysis and visualization of data. Area: Information and Communications. Training Provider: INTELLISOFT SYSTEMS.
Title: Data Analytics and Visualization with Python. Objective: Course Objective  Learners will be able to perform data analysis using Python to uncover new insights and patterns in data  Learning Outcomes • LO1 -  Learners will be able to implement data analysis processes to define hypothesis and solve problems. • LO2 - Learners will be able to analyse findings from data and evaluate data models. • LO3 - Learners will be able to conduct data exploration and statistical analysis. • LO4 - Learners will be able to conduct present data analysis results using visualization techniques. . Description: LU1 Introduction to Python programming • Define hypothesis to measures and test problems • Introduction of Python for data analysis  • Explore data types and usage   • Logical data modeling steps – control structures and functions  • Python modules and packages  LU2  Data Analysis    • Install Python Pandas library for data analysis  • Import and prepare data  • Qualitative and quantitative analysis   • Analyse findings to draw conclusion  • Evaluate data models  LU3 Data Mining and Statistical Analysis • Data mining techniques   • Hypothesis testing  • Statistical data analysis  LU4 Data Visualization and Storytelling • Data Interpretation  with data visualization  • Present data analysis findings with data storytelling. Area: Information and Communications. Training Provider: LAURES SOLUTIONS PTE. LTD.
Title: Data Analyst Programme. Objective: At the end of this course, learners will be able to: • LO1: Prepare collected data for analysis • LO2: Utilise data visualisation tools to represent initial insights obtained from data • LO3: Assemble data from multiple data sets based on identified patterns to facilitate analysis • LO4: Develop relevant insights from established data relationships and calculations  • LO5: Develop a machine learning model to facilitate further analysis • LO6: Develop data models for hypothesis testing • LO7:  Utilise statistical models to determine functional relationships among variables • LO8: Communicate insights from data to stakeholders using various visualisation techniques • LO9: Manage the integration of data and visualised insights in a Multi-cloud or Hybrid Environment. Description: Data Analytics is the detection, interpretation, and communication of meaningful patterns in data. It is an underlying component of digital dexterity. The ability to understand and communicate in a common data language is a core skill for a core technology. It is the difference between successfully deriving value from data and analytics and losing out to competitors who have made it a core competency in their organizations. At the end of this course, learner is expected to attain at least a “Proficient Level” in the Data Analytics Literacy Skill IQ measurement, and be able to meaningfully analyse customer activities and behaviours. Area: Information and Communications. Training Provider: YP ACADEMY PTE. LTD.
Title: Data Analytics Begins With Me. Objective: "Basic data analytics taught with a simple but powerful graphical user interface (GUI) drag-and-drop tool targeted at business analysts requires more sophisticated capabilities than what a spreadsheet can offer. Understanding and using data is increasingly an important part of an executive function. Whether you are a business analyst, customer service officer, physiotherapist, procurement officer or HR executive – the ability to understand where potential data is coming from, and to properly collect, clean, and analyse it, will be an important and critical skill. This course provides participants with the knowledge and skills to participate in the organisation’s data analytics process without any coding requirements. It will cover basic concepts of data analysis, processing of data, and visualisation of raw data and processed data to gain insights, followed by building simple models for classification and prediction. Participants will also learn to use the free and open source data analytics tool, Orange, a powerful and popular Python-based data analytics tool. Orange has a point-click-drag-and-drop GUI, no programming knowledge is required. At the end of the course, participants will be able to: • Understand the data analytics end-to-end workflow • Identify where the potential sources of data can come from • Use a GUI-based analytics tool to process, clean and prepare data for analysis • Perform basic analysis such as classification and predictions • Select the most appropriate analytics model to use for a specific task. Description: "Basic data analytics taught with a simple but powerful graphical user interface (GUI) drag-and-drop tool targeted at business analysts requires more sophisticated capabilities than what a spreadsheet can offer. Understanding and using data is increasingly an important part of an executive function. Whether you are a business analyst, customer service officer, physiotherapist, procurement officer or HR executive – the ability to understand where potential data is coming from, and to properly collect, clean, and analyse it, will be an important and critical skill. This course provides participants with the knowledge and skills to participate in the organisation’s data analytics process without any coding requirements. It will cover basic concepts of data analysis, processing of data, and visualisation of raw data and processed data to gain insights, followed by building simple models for classification and prediction. Participants will also learn to use the free and open source data analytics tool, Orange, a powerful and popular Python-based data analytics tool. Orange has a point-click-drag-and-drop GUI, no programming knowledge is required. At the end of the course, participants will be able to: • Understand the data analytics end-to-end workflow • Identify where the potential sources of data can come from • Use a GUI-based analytics tool to process, clean and prepare data for analysis • Perform basic analysis such as classification and predictions • Select the most appropriate analytics model to use for a specific task. Area: Information and Communications. Training Provider: NATIONAL UNIVERSITY OF SINGAPORE.
Title: Data Validation and Statistical Analysis with Programming. Objective: Perform the statistical validation component for the role of a junior data scientist, Statistician and/or Researcher. Validate dataset statistically to support data analysis and modelling Explore on data distribution and identify statistical anomalies Compute Statistical Indicators for the dataset Check normality assumption for a data series Perform statistical test and validation on dataset Conduct further statistical analysis. Description: Effective data science project relies on one critical aspect, data quality. With the right data quality, statistical analysis can then take place which translates data into meaningful insights for businesses. Data Validation plays an important role in ensuring the data is correct and useful in preparation for analysis. This course is designed to introduce the general concepts of Data Science with a focus on Data Validation and Statistical Analysis for participants who prefer a programming approach. By the end of this course, participants will be able to leverage on Python for Data Validation and Statistical Analysis. Area: Information and Communications. Training Provider: SINGAPORE UNIVERSITY OF TECHNOLOGY AND DESIGN.
Title: Data Validation and Statistical Analysis with Programming. Objective: Perform the statistical validation component for the role of a junior data scientist, Statistician and/or Researcher. Validate dataset statistically to support data analysis and modelling Explore on data distribution and identify statistical anomalies Compute Statistical Indicators for the dataset Check normality assumption for a data series Perform statistical test and validation on dataset Conduct further statistical analysis. Description: Effective data science project relies on one critical aspect, data quality. With the right data quality, statistical analysis can then take place which translates data into meaningful insights for businesses. Data Validation plays an important role in ensuring the data is correct and useful in preparation for analysis. This course is designed to introduce the general concepts of Data Science with a focus on Data Validation and Statistical Analysis for participants who prefer a programming approach. By the end of this course, participants will be able to leverage on Python for Data Validation and Statistical Analysis. Area: Information and Communications. Training Provider: SINGAPORE UNIVERSITY OF TECHNOLOGY AND DESIGN.
Title: Data Analytics and Visualisation Begins With Me (Classroom & Asynchronous). Objective: "This course aims to provide participants with a foundation in: • Overview of data and data analytics • How to read, work, analyse and argue using data   • Data as a language and common terminologies  • Data analytics process and project life cycle  • Tools and technology used for data analytics and visualisation • Machine learning and data visualisation techniques   At the end of the course, participants will be able to: • Apply data analytics techniques using tools through hands-on sessions • Apply data visualisation techniques using tools through hands-on sessions • Gain insights from data using various machine learning techniques • Work with and communicate using data ". Description: We live in a world that revolves around data. Data is regarded as the key resource that powers the world in the 21st century.  According to the International Data Corporation (IDC), many organisations still lack the data literacy skills to achieve business value.  Organisation that have accumulated a large amount of data would not see much benefits until it is harnessed to work for your organisation. Poor data literacy is one of the stumbling blocks and limits a company’s ability to reap value from data. To combat this, organisations will have to promote and develop their staff’s competencies in data.  Data literacy as defined by MIT is the ‘ability to read, work with, analyse and argue with data’. It is a set of core essential skills everyone in an organisation needs to use data effectively in their day-to-day activities and for decision-making. Used in the right way, data can help every employee achieve their objectives, perform their job better and contribute to overall company performance.  Employees who are data literate will also understand how to handle data appropriately, which will reduce the number of data breaches. Managers have to lead their organisations by setting the direction and the strategies on how data can be applied in the various parts of the organisation.   This course is designed for all professionals to gain the fundamental skills to understand and work with data for the organisation. This course aims to provide participants with a foundation in: •Overview of data and data analytics •How to read, work, analyse and argue using data   •Data as a language and common terminologies  •Data analytics process and project life cycle  •Tools and technology used for data analytics and visualisation •Machine learning and data visualisation techniques   At the end of the course, participants will be able to: •Apply data analytics techniques using tools through hands-on sessions •Apply data visualisation techniques. Area: Others. Training Provider: NATIONAL UNIVERSITY OF SINGAPORE.
Title: Data Validation and Statistical Analysis with Excel. Objective: Perform the statistical validation component for the role of a junior data scientist, Statistician and/or Researcher. Validate dataset statistically to support data analysis and modelling Explore on data distribution and identify statistical anomalies Compute Statistical Indicators for the dataset Check normality assumption for a data series Perform statistical test and validation on dataset Conduct further statistical analysis. Description: Effective data science project relies on one critical aspect, data quality. With the right data quality, statistical analysis can then take place which translates data into meaningful insights for businesses. Data Validation plays an important role in ensuring the data is correct and useful in preparation for analysis. This course is designed to introduce the general concepts of Data Science with a focus on Data Validation and Statistical Analysis for participants with non-programming backgrounds. By the end of this course, participants will be able to leverage on Excel for Data Validation and Statistical Analysis. Area: Information and Communications. Training Provider: SINGAPORE UNIVERSITY OF TECHNOLOGY AND DESIGN.
Title: Data Analytics and Visualization with Python (Synchronous e-learning). Objective: Course Objective  Learners will be able to perform data analysis using Python to uncover new insights and patterns in data  Learning Outcomes • LO1 -  Learners will be able to implement data analysis processes to define hypothesis and solve problems. • LO2 - Learners will be able to analyse findings from data and evaluate data models. • LO3 - Learners will be able to conduct data exploration and statistical analysis. • LO4 - Learners will be able to conduct present data analysis results using visualization techniques. . Description: LU1 Introduction to Python programming • Define hypothesis to measures and test problems • Introduction of Python for data analysis  • Explore data types and usage   • Logical data modeling steps – control structures and functions  • Python modules and packages  LU2  Data Analysis    • Install Python Pandas library for data analysis  • Import and prepare data  • Qualitative and quantitative analysis   • Analyse findings to draw conclusion  • Evaluate data models  LU3 Data Mining and Statistical Analysis • Data mining techniques   • Hypothesis testing  • Statistical data analysis  LU4 Data Visualization and Storytelling • Data Interpretation  with data visualization  • Present data analysis findings with data storytelling. Area: Information and Communications. Training Provider: LAURES SOLUTIONS PTE. LTD.
Title: Python for Data Analytics. Objective: Data science involves the collection, management, processing,  analysis, visualization, and interpretation of vast amounts of  heterogeneous data associated with a diverse array of  applications. Although it started over fifty years ago, data science  has become important in the last ten years because of society’s move  to an online lifestyle. Today, organisations have data on a wide  variety of our data activities, and data scientist has become one of  the most promising jobs in the 21st century. In this course, we will be  analyzing data using Python, an interpreted general-purpose  programming language. We will begin with using Python as a  calculator and moving on to performing basic statistical calculations  using Python and ending up with using Python to visualize data. Description: Topics covered: Using Python as a calculator. Basic data types: integers, floats, and  strings. Compound data types: lists, dictionaries, sets, and tuples.  Flow control: if … else, for loop, while loop, break and continue, pass.  Functions: arguments, recursion, Python modules and packages. File  operations: opening and closing files, reading file content. Python  date and time modules. The learner will be able to write and debug basic Python  programmes. The learner will also be prepared to take a data mining  module based on Python’s numpy, scipy and matplotlib libraries. Area: Information and Communications. Training Provider: NANYANG TECHNOLOGICAL UNIVERSITY.
Title: Data Validation and Statistical Analysis. Objective: By the end of this course, participants should be able to / can expect to: � Perform the basic ML model component for the role of a junior data scientist � Understand the types of data and databases in the business context � Appreciate the use of data dictionary and harness the potential of metadata for data science  � Acquire organizational dataset from data lakes and other democratized data sources for data enrichment purposes � Structure data into an appropriate form for data analysis � Manipulate data structures to support data-wrangling phase � Perform data wrangling on the acquired dataset � Impute concerning dataset to address quality issues and support machine learning phase � Address data quality issues with appropriate data cleansing technique � Iterate the data mining process progressively with the provision of data wrangling and exploratory analysis tools . Area: Engineering. Training Provider: SINGAPORE UNIVERSITY OF TECHNOLOGY AND DESIGN.
Title: Python for Business Analytics. Objective: This course will address the basics of Python programming and simple numerical analysis such as: - Scraping data from websites - Data cleaning and preparation - Exploring patterns and trends by descriptive analysis and data visualisations Case studies and practical exercises are provided throughout this course to demonstrate how the skills set above can help to gain business insights from data to solve real-world problems. Description: Data Science involves both a theoretical foundation of Statistics and Machine Learning and practical capabilities of implementation via programming. This module aims at equipping prospective business analyst with necessary programming skills in Python. The module starts with some Python programming fundamentals. It then walks through the following topics:  (1) acquiring data, including loading data from local sources such as databases and scraping data from the web;   (2) cleaning data;  (3) organizing data;  (4) summarizing data; and  (5) visualizing data. This completes the whole cycle of pre-analysis data processing. Area: Business Management. Training Provider: NATIONAL UNIVERSITY OF SINGAPORE.

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