The Data Science discipline has become one of the raging technological and advanced data learning domains aiding in building and developing new and innovative data-driven digital models creating innovational and transformational discoveries finding application in multiple sectors and top industrial practices. Such fundamentals and principles of data science and analytics are taught by different data science and analytics courses offered by industry-recognized platforms helping candidates acquire the certification and expertise for data science practices. Such a data science and analytics course is provided by the Khan Academy which is a popular learning platform globally. The Khan Academy data science course is a top-notch data learning and knowledge curriculum with advanced and latest modules in data science learning along with modules assessment sessions in online mode aiding learners to learn the course and finish it as per their flexibility.
To learn and apply such data science methodologies, the companies and the data professionals involved in the process inevitably have precise and accurate knowledge of some basic foundational concepts of statistical analysis, computation, computer science theories, applied programming skills, advanced mathematical learning along with information and communication technology.
A Must Read,
A Brief Overview of the Khan Academy Platform
The Khan Academy started in 2004 by Sal Khan is a top American-based educational and certification platform having the status of a non-profit academic organization with a diverse repository of advanced courses and learning modules from classes of fifth standard up postgraduate and certification courses with high accessibility.
The platform offers online tools and modules that educate global students and aspirants with study materials, assignments, and assessment tests along with video-enabled learning lessons in diverse academic subjects from science, commerce, arts, technology, medicine, and many more.
It empowers its learners through its self-study and flexible schedules with personalized and customized learning dashboards for the learners. It has some free tools for learners and instructors in 36 different languages and five different versions of the online learning site.
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Khan Academy Data Science Course: Detailed Syllabus and Program Features
Khan Academy data science course is one of the top-notch and skill-based online curricula with high technology and advanced learning in data science and analytics comprising of big data structures and data sets where one can learn about data methodologies and data analytics practices like data storage, dealing with spreadsheets and large database management.
1. It also includes a statistical analytical process to analyze large data structures and their patterns and market trends. It contains modules in data tools which consist of subsets as follows:
2. Storing data sets like text files and their formats, spreadsheet applications, and database storage curriculum with splitting techniques.
3. Computing basic statistics with statistical functions, numeric columns, filtering of data structures, data summarization by groups, and statistical approaches.
4. Identifying relevant patterns in big data sets like spotting market trends, conducting data visualization with charts and time series graphs, searching and spotting statistical fluctuations through line graphs, and more, making predictions and high-value assumptions.
5. The era of big data includes modules in big data learning that highlight the importance and significance of big data generated through various digital and online servers in terabytes and zettabytes and how it can be managed correctively subjecting them to storage and manipulation as per requirement.
6. The sources of big data structures and models consist of lessons on how big data structures are harnessed highlighting some of the top and relevant sources of such data models like through scientific research and LHC or Large Hadron Collider experiments producing around 60 petabytes of data annually. There are also digital libraries archiving many data records, medical records, user-based applications, and many more.
7. The challenges of big data discuss the challenges of data learning and practices through lessons like how data will be stored and processed efficiently to build high-technology business models and structures. It includes some techniques of data processing like faster algorithm structures, and usages of parallel computation for large data models along with its responsible usage techniques, and some ethical considerations.
There are also extensive sessions and orientation learning about programming in data science about its pseudocodes and variable structuring and storage along with extensive practices and knowledge of mathematical expressions, procedures, and constants, string operations through data variables including combining strings, slicing, chain operations through strings, application of pseudocodes.
It also consists of an evaluation of data algorithms measuring their efficiency, verification, solving critical data problems, and application of parallel and distributed computing having sub-parts like sequential computing and its comparison with parallel computing.
It also has sessions on data security and its importance along with brief modules on PII or Personally identifiable information along with data tracking practices through cookies, search history, etc.
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Top Courses and Programs of the Khan Academy Platform
The Khan Academy offers a wide range of courses and programs with lessons having enhanced curriculum including ranges from class 5th standard learning modules to postgraduate degree modules in diverse specializations from science, art history, finance, technology, computer science and programming, commerce, health and medical courses, engineering modules, business management and many more. It contains courses on the following subjects:
Mathematics: It contains different mathematical courses with high school modules like algebra and its foundation, geometry, integrated arithmetic lessons, and trigonometry learning lessons along with sessions in calculus, statistical analysis covering basics and advanced learning with practice questions for assessment.
Computer Programming: It includes extensive sessions on Java script learning, introduction, functions, logical statements, looping, arrays, object-oriented designing modules, and debugging techniques in programming. It also has high-end sessions in the introduction to HTML learning and CSS texting properties along with web development tools learning and layout techniques. It includes advanced sessions in Java scripting consisting of techniques like natural simulations, vectors, angular movement-making webpage interaction, and more.
Economics and Finance Learning: The Khan Academy offers courses on economics with lessons and curriculum on both microeconomics and macroeconomics aiding learners to garner knowledge and expertise to build a career in consultation, investment banking, financial and business analysis determining stabilization and open economy fundamentals, consumer and producer behavior and factor market.
Health and Medical Courses: It also includes some fundamental health and physiological learning modules with insights on human body structures, circulatory systems, anatomy, hematologic systems, disease learning, nervous system, etc. It enables students to even prepare for pre-medical examinations and learning.
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The Khan Academy Data Science Course With Statistics & Probability
The Khan Academy data science course has another important unit consisting of statistics and probability learning which has around 16 units with 157 skills one can develop through the completion of the module.
It starts with the basic modules of analyzing categorical data variables where one can develop the concept of variables along with exploration techniques of both quantitative and categorical data structures, reading and creating bar graphs, picture graphs, pie charts, etc.
It also has modules on the distributions in two-way tables with conditional distributions, marginal distributions, and more. Its next unit consists of representing and displaying quantitative data structures with the aid of frequency tables, dot plots, creation of histograms and stem and leaf plotting in statistical analysis.
The next unit consists of the means and ways of measuring quantitative data structures by introduction of key statistical terms like mean, mode, and median along with some statistical examples and sessions on calculation of the mean for data.
It also contains elaborate sessions of videos and lecture modules on the calculation of percentiles, analysis of frequency graphs and problem-solving sessions on it, normal distribution mechanism with Z scores, and its comparison with data transformations through linear techniques.
This unit also consists of sessions in density curves concept and calculation of median and mean through such density curve application along with work examples for further comprehension.
Unit 5, discusses in detail scatter plotting techniques and its process of examination of sets of quantitative data structures measuring the correlation of data variables through linear regression techniques. It also consists of lessons in outliers, clusters, and the construction of scatter plots.
It consists of probability modules with both theoretical and experimental learning and lessons on permutations and combinations of techniques in the subject. It offers introductory lessons on theoretical probability, simple probability, Monty Hall problem, basic operations, sets of intersections, and unions along with subsets, strict subsets, and supersets.
It consists further of lessons in addition to rule techniques for probability with Venn diagrams, sampling techniques with examples, and central limit theorem and confidence intervals.
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Curriculum in Machine Learning in Khan Academy Data Science Course
Khan Academy data science course consists of some comprehensive modules in machine learning practices for data science applications with key conceptual theories on machine learning reinforcement learning techniques of performance-based algorithms for AI and navigation tools.
It consists of supervised machine learning analyzing labeled data structures for classification and data prediction, unsupervised machine learning modules with pattern discovering by data clustering including recommendation systems, and more.
It also consists of units in neural networking techniques with inflow and outflow modules along with networking procedures with massive data structures aiding in image classification based on historical data structures and increasing automation efficiency of operational models.
Thus, Khan Academy data science modules cover the application and practical aspects of machine learning in detail helping aspirants to gain thorough learning and expertise in its practices with certification as well.
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Python Programming Language Modules in the Khan Academy
The Python programming modules of the Khan Academy have almost six exhaustive and comprehensive modules with introductory learning lessons on types of data variables, print data statements, and adding values with tracing program execution along with concepts of Python syntax, runtime, and logic errors in programming and debugging methodologies.
It has a top learning curriculum in variable designing with user input and through deploying arithmetic expressions like arithmetic operators, typecasting methodologies and built-in function sets with documentation techniques and input-output curriculum.
It has extensive Python languages like designing algorithms with conditional statements in programming showcasing how to evaluate Boolean conditions and expressions in Python programming with algorithms and selection modules, operator comparison, and other statements.
It has application learning with logical operators in Python, expressions, chained conditionals with if-else statements, nested conditionals with code review, and biases in algorithmic structures.
It also has recommendation sessions and instructional modules with practice sessions and looping techniques. It enables aspirants to have a real-life look and learning experience of using Python dictionaries in solving advanced data science problems with reference documents and nested data structures.
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SQL Learning and Querying Modules in the Khan Academy Data Science Course
SQL learning modules are also included in the Khan Academy data science course essential for data storing and querying in data manipulation and evaluation processes. It emphasizes SQL being a special-purpose programming language with relational database structure finding its application in the development of high-technology data structures and business-oriented data models.
It starts with introductory sessions on SQL learning and the creation of tables and the insertion of data information in such tables along with advanced querying practices. Apart from the SQL basic modules, it also includes advanced modules in SQL learning including complex query solving with IN sub-queries learning with project work and examples of data querying practices.
It has both relational queries learning with splitting data structures into tables and practicing joining techniques in SQL analysis and modification of databases learning by table alteration practices, usages of updating and deletion techniques along with other practices.
Such a comprehensive lesson in SQL practices is highly beneficial for all aspirants in data science as SQL practices is a core data science and data analytics module.
Top Career Options to Pursue From the Khan Academy Data Science Course
The Khan Academy data science course offers diverse opportunities to pursue data science-based jobs roles enabling the learners to forge a lucrative career path in the data science domain having high perks and benefits along with professional growth opportunities.
Some of the top career options in data science learning include:
Machine Learning Engineer: A machine learning engineer is a top profile in this domain where one is expected to have thorough knowledge and practices based on machine learning techniques, its regression modules, and classification and clustering along with the application of essential tools for automation of high operating structures in industries and various sectors.
They comprehend specific business objectives and analyze machine learning algorithms to solve problems along with supervision of data processes, defining data augmentation pipelines, validating essential strategies, and deploying machine learning models in developing industrial structures and business models.
To become a machine learning engineer, one must be proficient in deep learning framework along with Python libraries and essential tools like tensor flow, sci-kit learn hardware selection for running ML modules, etc.
Big Data Engineer: A big data engineer is responsible for the collection and advanced analysis of large amounts of data structures and implementing ETL methodology for importing and exporting data structures to build efficient data structures for industrial operation usage.
They are mainly responsible for monitoring data performance and analyzing data retention policies of the big data structures integrating big data tools and programming software.
To become a data engineer, one must have years of experience in analyzing data patterns and working with enhanced data models with proficiency in distributing principles, Hadoop tool and HDFS, spark, NoSQL databases, lambda architecture, machine learning tool kits like MAHOUT and Spark ML, etc.
Data Architect: A data architect is another significant data science job role and responsibility mainly dealing with the processes and techniques of outlining advanced and integrated data designs and models, integrating business strategy with data architecture along with testing and analyzing databases and advanced data pipelines.
They implement organizational strategy along with designing data models and data warehousing and data wrangling techniques in data science and analytics along with managing data architecture and offering end-to-end solutions.
They ensure a high level of data accessibility and data security along with designing efficient and resilient data models.
They must have proficiency in systems development, data lifecycle processes, knowledge of data mining, ML and Python, and C++ programming language. They must also be acquainted with natural language processing techniques, predictive data modeling, and data visualization methods.
Database Administrator: A Data Administrator is a top and demanding data science job role dealing with huge sets of digitally produced data structures and models optimizing database performance and troubleshooting technical and significant data-based problems like their connectivity, and data integrity along with anticipating and developing future databases for model structuring and business data modeling.
They also deal with maintaining backup data structures, disaster recovery, and offsite data management along with importing, exporting, transformation, cleansing, and other significant data analytic practices.
To become a database administrator, one must have degree knowledge in computer engineering and computer science along with high experience with data structuring and knowledge of SQL, database theories, reporting tools in data science, and certification in PostgreSQL.
Business Intelligence Developer: A Business Intelligence developer is a flourishing data science and analytics job role with huge demand in the job market dealing with AI modules and programming.
They manage top business intelligence infrastructure and tools and submit business reports to data analysts, data scientists, and data engineers to further develop data query solutions and data restructuring.
A business intelligence developer normally retrieves high-value data reports and maintains them for reference creating significant data reports and modules out of it.
They must have a good comprehension of data schemas and data modeling tools along with practical knowledge of BI tools like Tableau, Cognos, Power BI, Qlikview, Spotfire, Looker, Sisense, and more. They must be well acquainted with querying techniques like query filing, optimization, and more.
Data Scientist: The most common and prominent role in the data science domain is that of a data scientist who collects large data structures and applies data analytical processes like data evaluation, data wrangling, data pre-processing, and data visualization to interpret their patterns and behaviors along with conducting scientific research of such data models to develop relevant hypotheses along with answer solutions.
They generally use certain data analytical tools and practices like programming languages like Python along with Machine learning techniques, SQL querying and deep learning, statistical analytic techniques, and more. A data analyst with relevant years of experience and skill development can be eligible to become a data scientist.
FAQs:
1. Is Khan Academy’s data science course a good data science program?
Ans: Yes, the Khan Academy data science course is a highly comprehensive and detailed data science and analytics program with top industry recognition and covers advanced modules in the domain.
2. What are the top skills one can learn from the Khan Academy data science course?
Ans: The Khan Academy data science course enables one to develop strong foundational knowledge in data science along with programming, machine learning, SQL querying, and networking, Power BI with advanced modules in high-end data methodologies, and case studies examples to get practical expertise as well.
3. Can a software engineer become a data engineer after pursuing a data science course?
Ans: Yes, a software engineer can switch careers into data engineer successfully by going through and learning all the fundamentals and advanced knowledge curriculum in data science and analytics in its course structure.
4. Is prior knowledge of probability required for data science learning?
Ans: Since probability modules come under statistical learning, it is important to have some knowledge of probability however the data science courses cover probability modules in detail for further learning.
Conclusion:
Thus, the Khan Academy is a top and popular data science learning platform offering advanced modules in data analytical practices with practical and self-assessment modules to monitor one’s progress through the entire learning course.
Apart from data science, it offers sub-categorical course modules in data analytical tools and software enabling one to gain more experience and knowledge of data analytical practices. So, if you are interested in learning and getting certified in data science, you can enroll in the data science module of the Khan Academy.
