A Comprehensive Guide To IBM Data Science Course

Data science is the systematic use of scientific methods, algorithms, and statistics for the analysis of large datasets and deriving insights for decision-making and planning. It’s increasingly critical to businesses: The knowledge that data science provides includes opportunities that enhance operational efficiency, discover new business prospects, and enhance marketing and sales initiatives, among others. In the end, they may result in the achievement of competitive superiority against business competitors. This shows that having the skills in data science is crucial in today’s day and age. The online IBM data science course is a great choice to learn these skills.

What is Data Science?

Data science encompasses a broad range of activities such as data engineering, data pre-processing, data and knowledge discovery, data and predictive modeling, learning, and visualization and it includes sub-disciplinary fields from statistics, mathematics, computational intelligence, software engineering, and others.

This is usually undertaken by competent data scientists though junior data analysts may be involved in the process. Furthermore, it is noteworthy that many organizations partially draw on citizen data scientists, which may be BI specialists, business analysts, employees with strong data literacy, data engineers, and other workers, who do not have data science training.

What Is The Importance Of Data Science?

Data science is a crucial component in almost every aspect of business processes and management strategies. For example, it offers information on the customers that indicates which marketing strategies could be employed to develop better marketing tactics for promoting the products more, and efficiently.

For financial risk management, it can help in identifying fraudulent transactions while in industries such as manufacturing it can assist in identifying when machines in the production line are likely to break down. That is how it assists in preventing cyber-attacks and other security threats within the IT systems.

In terms of operations, data science can help improve how organizations manage supply chains, stock control, distribution, and customer relationships. In an even deeper sense, they provide a clear indication of increased efficiency and concomitant reduction in costs.

Data science also helps organizations develop strategies and specific business models based on customer and competitor analysis and business environment trends. It also emphasizes that businesses are likely to miss some opportunities and make wrong decisions in their absence.

Data science is also crucial in industries or sectors that are outside the mainstream commercial activities for instance. Its functions in health sectors include a diagnostic tool, image interpretation, procedure planning, and medical studies.

Some of the ways in which academic institutions currently apply data science include tracking the performance of students and enhancing the sales, that is marketing, of their institutions to prospective learners.

Data science is applied in sports, with teams collecting data on players and game strategies to use during the games. Among those in the heavy user category, government agencies and organizations that deal in public policies are also included.

Benefits of Data Science

In a broad sense, one of the key advantages of data science is to enhance and support decision-making. It is particularly valuable to organizations since they can integrate asset value, which is backed by quantifiable data, into their business calculations.

Quite possibly, the employers’ decisions based on the collected data will result in better business outcomes, effective spending, and efficient business operations and procedures. It can, therefore, be said that the exact business values that come with data science are relative to the organization and the nature of the industry.

Data Science also assists in defining what specific target market needs to be addressed or can be addressed in customer-facing organizations. Since marketing and sales teams are likely to have direct access to customers, they should make use of customer data to reduce conversions and generate customer-specific marketing concepts and promotional offers that will increase sales.

In other cases, the advantages include lower fraud levels, reduced risk, higher financial trading returns, increased manufacturing availability, enhanced Supply Chain performance, more robust Cyber Security, and better patient care. Data science also allows for fast processing as data arises in the process of data analysis.

Skills Required for a Data Scientist

A data scientist is skilled and experienced in data analysis, statistics, and computer science and is responsible for analyzing large amounts of structured and unstructured data for the purpose of identifying trends and patterns that will help a company and or business organization to improve its existing products, develop new ones or provide better services to its clients.

The core work of data scientists is to process data which may be big data and try and extract useful information to be passed to business executives, managers, workers, government administrators, physicians, researchers, and virtually every other person with a need for data.

They also develop methodologies, algorithms, and applications of Artificial Intelligence for use in different areas. In both cases, they obtain data, build the models for analysis, and then follow the process of training, testing, and using models on the data.

Therefore, data scientists should have data preparation, data mining, predictive modeling, machine learning, statistical and mathematics expertise as well as knowledge of algorithms and coding, coding languages, such as Python, R, SQL, etc. Some companies also require them to generate data visualizations, dashboards, and reports that present insights generated by analytics.

However, apart from technical skills, data scientists need some non-technical skills, and these include; business acumen, curiosity as well as critical thinking skills. Another useful skill is the ability to show the information and its implications to the leaders and other business-oriented people in simpler terms. That includes the ability to tell a story with data in the context of using data visualizations with a prepared narrative text that is part of the presentation.

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Roles in Data Science

A large number of companies have followed the approach of having a dedicated team or multiple teams responsible for data science initiatives. It includes the following positions:

Data engineer- Some of the duties are to establish data pipelines and assist with data transformation and model deployment, thus collaborating with data scientists.

Data analyst- This is a more junior level position for analytics specialists who have worked their way through a larger firm or are not as skilled as a Data Scientist.

Machine learning engineer- This is a programming position and entails the development of the machine learning models required in data science solutions.

Data visualization developer- They collaborate with data scientists to develop interactive visualizations and business intelligence dashboards through which analytics findings can be reported.

Data translator- Another one is an analytics translator; it is a relatively new position that deals with business departments, co-plans the activities, and relays information.

Data architect- A data architect is responsible for the development and management of the data to the infrastructure for analyzing needs.

The roles of data science teams can be either central, meaning being a part of an enterprise-wide function and located at the corporate level, or local, meaning being a part of an individual business unit or a hybrid of both.

How Different Industries Use Data Science

Google and Amazon are startups that initially implemented data science and big data analytics for their operation and were joined by other Internet and e-commerce enterprises, such as Facebook, Yahoo, and eBay. Now, data science is used in various companies and organizations irrespective of their type or size. Here are some of the sectors in which it’s used:

Entertainment- The availability of data science facilitates the streaming service industry to monitor the usage of its services and the shows that are watched by the viewers, which determines the kind of TV shows and films produced. Recommendations are also generated from algorithms developed from such databases and tend to be based on a user’s preferences in watching programs.

Financial services- The banking industry, credit card companies as well as other traders and merchants, need to sift and analyze large amounts of data to identify fraudulent patterns, estimate credit risks on loans and credit limits, as well as, categorize customers’ credit portfolios to offer them suitable products for upgrading to premium ones.

Healthcare- Additional data science components being incorporated in such healthcare facilities include utilizing ML algorithms and models to process X-ray scans and assist physicians in identifying diseases and possible treatment plans using previous experiences of other patients.

Manufacturing- Manufacturers use data science in supply chain and distribution to increase efficiency, not forgetting the aspect of maintenance where specialized algorithms help identify possible equipment failure in plants.

Retail- Companies look at consumer trends and their purchase decisions to influence consumption trends on relevant products, marketing and advertising, and, sales and promotions. Data science also helps them in stock management of products and supplies to ensure they have the products in stock as much as possible.

Transportation- Third-party logistics services, delivery agents, and freight carriers apply data science in planning delivery schedules, logistics locations, and best means of transport with regard to shipments.

Travel- Data science helps airlines in everything from planning flights down to the most minimal details of the flight paths that should be taken and when, the crew scheduling, and how to load the airplane with passenger traffic. A time and schedule-based algorithm also controls the variable pricing of flights and hotel rooms.

Other data science applications in areas like security, customer support, and managing business processes are more widespread across industries. An example of the latter is assisting in employee recruitment and talent acquisition: It may reveal trends that are characteristic of high performers, the degree of success of advertised jobs to generate responses, and other useful data for selection.

Data Science Tools And Platforms

Software architectures like data repositories and analytics engines including Spark, Hadoop, and NoSQL databases. The new generation programming languages like Python, R, Julia, Scala, and SQL.

Analytical tools like SSPS and SAS for statistical analysis of data collected from the field. Metadata analysis tools and open-source machine learning frameworks and libraries such as TensorFlow, Weka, Scikit-learn, Keras, and PyTorch.

The Jupyter Notebook is a browser-based interactive environment for sharing documents with code, math, text, tables, images, links, and conferencing or online presentations.

In this context, some of the most widespread data visualization tools and libraries used for data visualization are popular: Tableau, D3, and many others. It is also important to note that there are many types of data science software, developed and marketed by various vendors, which can be significantly different in terms of available features and capabilities.

Specialized analytics platforms for professional data scientists, turnkey and managed machine learning platforms suitable for citizen data scientists as well as collaboration and workflow centers for data science projects.

The list of vendors comprises Alteryx, AWS, Databricks, Dataiku, DataRobot, Domino Data Lab, Google, and H2O. The other dominant players in the field include IBM, Knime, MathWorks, Microsoft, RapidMiner, SAS Institute, Tibco Software, and others. The IBM data science course will cover the use of many of these tools.

List of Top 10 IBM Data Science Course

Below we have mentioned the details of the IBM data science course.

1. Tools and Techniques of Data Science

This IBM data science course introduces the main concepts underlying data science and teaches students how to use open-source tools and resources to get started on their data science journey.

During the course, students will learn about Python, Jupyter Notebooks, SQL, Docker, and a variety of open-source data sources. The learner will also be introduced to the core techniques of data science, AI, and machine learning in the IBM data science training program.

Course Features

• Basic level
• Self-paced
• Duration- 1 hour
• No cost
• Rating- 4.5/5

2. Applied Data Science Capstone

The IBM data science course is designed to help you prepare a report for stakeholders after demonstrating proficiency in data science and machine learning techniques on real-world data collection.

Write Python code to create machine learning models such as support vector machines, decision tree classifiers, and k-nearest neighbors.  At the end of this IBM data science training program, you can evaluate the outcomes of machine learning models for predictive analysis, evaluate their strengths and limitations, and select the best model.

Course Features

• Rating- 4.7/5.0
• Intermediate level
• Duration- 13 hours (approximately)
• Self-Paced

3. Data Science with R – Capstone Project

With the help of this IBM data science course, you can learn to create a web scraping application that extracts data from an HTML file using HTTP requests and converts it to a data frame.

Further, you can learn to prepare data for modeling by handling missing values, formatting and standardizing data, binning, and converting categorical variables to numeric values.

Exploratory data analysis approaches include computing descriptive statistics, visualizing data, and generating correlation statistics. Create a Shiny app with a Leaflet map and an interactive dashboard.

Course Features

• Rating- 4.6/5.0
• Intermediate level
• 24 hours (approximately)
• Self-paced

4. Fundamentals of Data Science with IBM Cloud

This IBM data science course focuses on IBM Cloud Pak for Data as a Service, a web-based solution that is completely managed by IBM. However, the customer can dynamically acquire the tools and services, giving them a low-maintenance, extremely cost-effective solution.

The BM data science training program covers the fundamentals of introducing data into the cloud, changing that data, and evaluating it. It concentrates on creating machine learning solutions and publicizing and monitoring them.

Course Features

• Basic level
• Self-paced with labs
• Duration- 3 hours
• No cost

5. Tools for Data Science

This IBM data science course describes the important tools used in data science like libraries, data sets, machine learning models, and big data. You will be taught to use languages typically used by data scientists, such as Python, R, and SQL.  And also learned to create and manage data science source code with Git repositories and GitHub.

Course Features

• Ratings- 4.5/ 5.0
• Beginner level
• Duration- 18 hours (approximately)
• Self-paced

6. Python for Data Science, AI & Development

Through this IBM data science course, professionals can learn Python for data science and software development. Python programming logic includes variables, data structures, branching, loops, functions, objects, and classes.

You will also be taught how to use Python libraries like Pandas and Numpy, as well as build code in Jupyter Notebooks, effectively.

Course Features

• Rating- 4.6/5.0
• Beginner level
• Duration- 25 hours (approximately)
• Self-paced

7. Statistics for Data Science with Python

In this  IBM Data science course you can learn to write Python code to perform various statistical tests such as the T-test, ANOVA, and regression analysis. Along with following hypothesis testing, interpret the findings of your statistical analysis.

Write Python code to calculate descriptive statistics and visualize the results.  At the end of the IBM data science training program, create a final project that illustrates your understanding of various statistical tests in Python.

Course Features

• Ratings- 4.5/5.0
• 14 hours (approximately)
• Self-Paced

8. SQL for Data Science with R

With this IBM data science course students can learn to create and access a database instance in the cloud. Create and execute basic SQL queries, including SELECT, INSERT, UPDATE, DELETE, CREATE, and DROP.

Create SQL statements to filter, sort, and group results, use built-in functions, write nested queries, and access multiple tables. Along with learning to analyze data from Jupyter using SQL and R by querying real-world datasets.

Course Features

• Ratings- 4.3/5.0
• Beginner level
• Duration- 27 hours (approximately)
• Self-paced

9. Data Science with Python

In this IBM data science course, you will learn to analyze data in a database using SQL and Python, create a relational database, and use DDL commands to manipulate numerous tables.

Use DML commands to create basic to intermediate-level SQL queries.  And finally create more sophisticated queries by utilizing advanced SQL techniques such as views, transactions, stored procedures, and joins.

Course Features

• Ratings- 4.6/5.0
• Beginner level
• Duration- 20 hours (approximately)
• Self-paced

10. Python Project for Data Science

Through this IBM Data Science course students can assume the role of a Data Scientist / Analyst by working on a genuine project. You will learn to demonstrate your proficiency in Python, the preferred language for data science and analysis, along with applying Python foundations, Python data structures, and data manipulation in Python.

In the IBM data science training program you can also learn to create a dashboard with Python and libraries such as Pandas, and Plotly in Jupyter Notebook.

Course Features

• Rating 4.5/5.0
• Intermediate level
• Duration- 8 hours (approximately)
• Self-paced

Frequently Asked Questions

Does data science require coding?

Data scientists need to have coding knowledge of languages like SQL, R python, and many more. They employ these coding languages in management tasks such as statistics, data mining, and presentation purposes. Adding the coding language or more to your CV will always advance your employability chances to the next level.

What opportunities are available to you if you are proficient in data science?

Some of the areas where data science professionals can work include data analysts, database administrators, systems analysts and business intelligence specialists, digital marketers, data scientists, and financial analysts. Learning through the IBM data science training program is going to open up several opportunities in the job market. Since the current corporate world revolves around the management and processing of data, anyone with skills can find a job within a short time easily.

What are the goals of data science?

Data science makes it possible for people and companies not only to gather data but to make sense of it. The data scientists normally analyze raw data to identify different patterns or trends that exist within them. It involves the use of tools and strategies in providing and helping businesses how they could come up with the right decisions and be successful.

Conclusion

As stated in this article, data science is a highly paid profession with many opportunities and positions. Nevertheless, in order to reap such benefits, one must be equipped with the necessary skills. Taking up an IBM data science course will not only help you acquire the necessary knowledge and skills to work in the researched field of data science but also prepare you for its stiff competition. It will provide you with theoretical knowledge and practical training, to be ready for a real-life job as a data scientist.