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Data has now become the most important thing for the running businesses. Small and large businesses are working on the concept of data-driven decision-making abilities. At this point data science and analytics become important. Data Analytics is becoming one of the most demanding domain in Computer Science. Along with Data Science, their technologies are evolving. These technologies continue to lead the way in extracting valuable insights from a raw data matrix and facilitating the expansion of businesses.
Lesson 1 – Introduction to Data Science (Duration-2hrs)
• Definition and scope of data science
• Exploration of the importance and diverse applications of data science across various industries
• Examination of the historical background and evolutionary trajectory of data science
Lesson 2 – Statistics (Duration-4hr)
• Overview of basic statistical concepts including measures of central tendency and variability
• Introduction to probability distributions
• Differentiation between descriptive & inferential statistics
• Introduction to hypothesis testing methodologies and significance levels
Lesson 3 - Information Visualisation (Duration-3hr)
• Understanding Data Visualization, its tools & libraries such as Matplotlib, Seaborn, etc.
• Techniques for data visualization, distributions, relationships, and patterns
• Interpretation of visualizations to extract valuable insights from the data
Lesson 4 - Data Mining, Structures, and Manipulation (Duration-5hrs)
• Understanding the basics of data mining and its importance in extracting valuable insights from massive datasets.
• Exploring the process of data mining and its applications in various industries.
• Discussing the challenges & ethical guidelines in data mining.
• Comprehension of the attributes and utility of lists and tuples
• Execution of operations to manipulate lists and tuples
• Utilization of list comprehensions for streamlined data manipulation
• Introduction to dictionaries and sets as primary data structures
Lesson 5 - Algorithms used in Machine Learning (Duration-4hrs)
• Introduction to machine learning & its algorithms
• Implementing supervised and unsupervised learning techniques
Lesson 6 - Data Acquisition & Data Science Life Cycle (Duration-4hrs)
• Examination of data sources and categorization into structured and unstructured data
• Survey of diverse data collection methods
• Exploration of various data storage formats such as databases, spreadsheets, JSON, and CSV
• Deconstruction of the data science process into key stages
• Identification of the pivotal role of a data scientist at each stage of the lifecycle
• Illustration of the data science lifecycle
Lesson 7 – Data Pre-processing (Duration-4hrs)
• Illustration of data cleaning & pre-processing techniques
• Strategies for handling missing values and outliers in datasets
• Introduction to feature engineering methods for creating new data attributes
Lesson 8 - Experimentation, Evaluation and Project Deployment Tools (Duration-6hrs)
• Overview of popular data science tools and platforms including Jupyter Notebook, RStudio, and Anaconda
• Introduction to version control systems such as Git for collaboration and project management
• Guidance on setting up a data science environment, including installation and configuration of necessary tools and libraries
Lesson 9 - Predictive Analytics and Segmentation using Clustering (Duration-4hrs)
• Introduction to clustering algorithms for grouping similar data points together.
• Exploring unsupervised learning algorithms such as k-means clustering and hierarchical clustering.
• Discussing methods for evaluating the quality of clustering results.
• Exploring machine learning algorithms for predictive analysis.
• Applying advanced techniques to analyze real-world datasets.
Lesson 10 - Working on Real World Projects (Duration-4hrs)
• Engaging in hands-on projects involving real-world data visualization tasks.
• Applying acquired skills & techniques to tackle data-centric challenges.
• Presenting project outcomes and insights to peers for constructive feedback and discussion.
Lesson 11 - Big Data Fundamentals and Hadoop Integration with R (Duration-5hrs)
• Getting familiar with big data and why it's important in today's world.
• Learning about the main features of big data: how much there is, how fast it comes, how varied it is, and how reliable it needs to be.
• Exploring the challenges and opportunities that come with working with big data.
• Discovering the different parts of the Hadoop ecosystem and what they do.
• Understanding how Hadoop's file system helps store lots of data across many machines.
Lesson 1 - Introduction to Data Analytics (Duration-1hr)
• Recognizing the significance and practical applications of data analysis.
• Introducing fundamental concepts and principles essential for proficient data analysis.
• Surveying prevalent software and tools employed in the field of data analysis.
Lesson 2 - Introduction to R (Duration-1hr)
• Understanding R language syntax and structure
• Getting started with RStudio IDE
• Fundamentals of variables, data types, and operations in R
• Control structures: loops and conditions
Lesson 3 - R Basics (Duration-5hrs)
• Exploring vectors, matrices, arrays, and data frames
• Manipulating and accessing different data structures
• Applying functions to perform operations on data structures
Lesson 4 - R Packages (Duration-2hrs)
• Utilizing dplyr for data manipulation tasks
• Tidying data with tidyr: reshaping and transforming
• Filtering, selecting, arranging, and summarizing data efficiently
Lesson 5 - Importing Data (Duration-1hr)
• Importing and exporting data from various sources
• Reading and writing data from/to databases
• Managing different file formats in R
Lesson 6 - Manipulating Data (Duration-1hr)
• Advanced data manipulation techniques
• Working with time-series data and forecasting
• Introduction to NLP and text mining
• Integration of R with other tools for advanced analytics
Lesson 7 - Error Metrics (Duration-3hrs)
• Implementing data cleaning methodologies to fix errors.
• Strategizing category wise data handling through encoding and transformation techniques.
Lesson 8 - Machine Learning (Duration-5hr)
• Understanding artificial intelligence.
• Exploring the history and evolution of artificial intelligence.
• Surveying the diverse applications of artificial intelligence across various industries.
• Discussing the ethical implications and societal impact of artificial intelligence.
• Introducing the fundamental principles of machine learning.
• Understanding the difference between supervised, unsupervised, and reinforcement learning.
• Exploring key machine learning algorithms such as linear regression, decision trees, and k-nearest neighbors.
Lesson 9 - Supervised Learning (Duration-6hrs)
• Understanding supervised learning concepts and objectives.
• Implementing regression algorithms for predicting continuous outcomes.
• Exploring classification algorithms for predicting categorical outcomes.
Lesson 10 - Unsupervised Learning (Duration-4hrs)
• Understanding unsupervised learning methodologies.
• Exploring clustering algorithms like K-means clustering and hierarchical clustering.
• Dimensionality reduction techniques.
• Applying unsupervised learning for data exploration & pattern recognition.
Lesson 11 – Natural Language Processing (Duration-7hrs)
• Understanding and working with human language in computers.
• Discovering how NLP has evolved over time and where it's used today.
• Exploring how NLP helps in different fields like healthcare, finance, and customer service.
• Getting started with preparing text for analysis.
• Learning simple methods to break text into smaller parts, find root words, and fix common mistakes.
• Understanding why we remove certain words and punctuation from text and how we make spelling consistent.
• Diving into more advanced topics in NLP, like answering questions, having conversations, and understanding language better.
• Exploring new ideas and technologies in NLP, like big language models that learn from lots of text.
• Trying out what we've learned in practical projects and seeing how NLP can help solve real-world problems.
Lesson 12 – Deep Learning (Duration-6 Hours)
• Basic understanding of deep learning and neural networks.
• Investigating the historical context and evolution of deep learning.
• Exploring the diverse applications and significance of deep learning across various domains.
• Familiarizing with artificial neural networks (ANNs) and their architecture.
• Understanding neurons, layers, and activation functions.
• Delving into the mechanisms of feedforward and backpropagation algorithms employed in training ANNs.
• Surveying prominent deep learning frameworks such as TensorFlow and PyTorch.
• Hands-on engagement with constructing and training neural networks using these frameworks.
• Merits and limitations of different deep learning frameworks.
Data analysts are something of the need of the hour in today's fast world, inside enterprises. Organizations realize today’s importance of data analysis which helps make better decisions and leads to successful commercial pursuits. Undeniably, one can see a growing need for a Data Analytics Course in India, viewed as one of the nation's hotbeds for any kind of technology. To start a fulfilling career in data analytics, the ideal course would be a data analytics course in India. Data analytics has brought the importance of data management since, today information is worth much and is utilized by organizations to acquire a competitive advantage. In this way, consequently, data analysts help to support the success of the organization through the provision of valuable information. India, a growing technology hub has seen increasing demand in the area of data analytics. Almost all companies have been putting a huge investment in high technologies, and the requirement for trained data analysts who can provide the company with the necessary data to run a successful business is being raised. Choose GICSEH in case you want to enrol yourself on data analytics training in India. It's gaining more and more popularity for equipping students with practical skills and resources to help them pursue a lucrative career in data analytics.
Today, firms rely considerably on data analysts for reasons like gathering, processing, and presenting data in a meaningful way with information that can be utilized to operate a successful organization is the primary responsibility of a data analyst. Furthermore, data could be used to make strategic decisions that may leave you at an advantage over your competitors. Data is what informs strategic decisions. Thirdly, it ensures better decision-making, customer experience, and operational efficiency. With all these technological breakthroughs, data analysts will now be needed in every business from manufacturing and finance to e-commerce. In the future, the contribution of Data Analytics to the eventual formation of business informational strategy will rise and be more diversified.
The entire courses offered by the Data Analytic Training Institute in India are designed for the rising demand of this industry. Those who seek knowledge about data analysis can learn from or get an entire course from GICSEH. The need for data analysts is on the rise, and they can earn good compensation in the largest corporations, financial institutions, tech startups, and multinational corporations. Courses allow participants to have practical experience with tools and methods to apply in solving problems from real-world applications, including machine learning, instruction about data analytics is a part of the course. Users get various benefits while attending a data analytics course. The Data Analytics Institute in India will provide the students with in-depth knowledge about concepts related to data analytics, ranging from advanced analytical methods to data collection methods. Another benefit of enrolling in Data Analytics Course in India includes networking that introduces its pupils to business executives and arranges to have them participate in several workshops, internships, and other events. In such a scenario, GICSEH could be the best institute for any person who has the desire to enter the competitive employment market with data analytics skills alone.
Courses offered at Data Analytics Institute in India is completely designed on the terms of requirements of this growing market. GICSEH offers full courses along with training to those who are interested in gaining experience in data analysis. Not only is the demand for a data analyst up, but it also offers high-paid jobs in the largest organisations along with multinational companies, financial institutions, and tech startups. The course includes data analytics training to help users gain hands-on experience with the tools and techniques available to combat real-world problems, including that of machine learning.
When enrolling in a data analytics course, such as those offered by GICSEH in India, the key learning objectives typically include:
1. Data Foundations
• You will understand the basics of data types, data collection and data management.
• Also you will learn how to cleanse, organize and pre-process collected data and/or raw data before applying data analytics.
2. Comfort with Data Analytics Tools/Techniques
• In the course, you will gain experience using data analytics tools, including Excel, SQL, Python, R, Tableau, and Power BI.
• You will apply statistical methods, produce exploratory data analysis and derive various levels insights about the data
3. Data Visualization and Reporting
• You will learn to produce effective and clear data visualizations that communicate the insights from data analysis.
• You will also learn to generate dashboards and written reports for various stakeholders.
4. Machine Learning and Predictive Analytics
• You will gain an understanding of various machine learning algorithms, and how it relates to data analytics.
• You will build predictive models for the various business decisions being made in the case study.
5. Problem-Solving with Actual Data
• Data analytics applications through case studies and projects designed from actual organizational challenges.
• You want to present actionable recommendations based on the data that would result in growth results organizationally.
6. Enhancing Business Acumen
• Learn how data analytics supports decision-making processes in various industries such as finance, manufacturing, and e-commerce.
• Understand the role of data analytics in improving customer experience and operational efficiency.
7 .Developing Communication and Collaboration Skills
• Improve the ability to present complex data findings clearly to both technical and non-technical audiences.
• Collaborate effectively within cross-functional teams to drive data-driven initiatives.
These objectives ensure that learners are well-prepared to enter the data analytics field with a strong foundation, practical skills, and the confidence to contribute to organizational success.
For those who want to pursue a rewarding career, enrolling in a data analytics course in India offers several advantages. These courses will provide students with a thorough understanding of the fundamentals of data analytics, covering everything from data collection to the most sophisticated analytical methods. The networking opportunities offered by the Data Analytics Course in India are an additional advantage. A reputable institution will introduce its students to business executives and arrange for them to participate in workshops, internships, and other events. Therefore, GICSEH can be the finest institution for everyone who wants to learn data analytics and get into the competitive employment market.
GICSEH's Data Analytics Course offers more than simply technical instruction. Students can use data sets from retail, healthcare, and finance to work on real-world initiatives. Employable skills and market demands are the main topics of the job-oriented courses. The pupils have the opportunity to enter a profession. Excel, SQL, Python, Tableau, Power BI, and the principles of machine learning are among the industry-standard tools that the students have practical experience with. Certified trainers oversee and guide the use of tools by students. Due to the institutes' extensive network, internships and associated work experience are consistently accessible, and placement assistance is 100% guaranteed.
Lab hours are offered around-the-clock. Students can choose from offline, online, or hybrid class schedules in addition to LMS assistance, and they can earn an industry-recognized data analytics certification.
The training stresses critical soft skills that have a significant impact in the workplace in addition to technical expertise. Both technical and non-technical audiences can benefit from the learners' effective communication skills and insights. Students are urged to think critically and solve problems creatively so they can see business challenges from a variety of perspectives. They engage in both individual and group initiatives, which enhances their cooperation and teamwork. Additionally, learners receive training in creating presentations and dashboards that tell difficult data stories visually. This is a special tool that will increase their capacity to deliver data narratives. In order to manage several projects and get ready for the duties of a contemporary, data-driven professional, students lastly participate in time management training.
Businesses are dealing with a rapid digital shift as 2025 draws near. The transition of organizations of all sizes to the digital age is happening more quickly. Both start-ups and multinational corporations now rely heavily on data when making decisions. In an effort to stay ahead of the competition, businesses are switching from intuitive strategies to data-driven models that optimize consumer experience and perform more effectively. Because of this, there is an extraordinary need for people with a data analytics skill set in every industry, including government, retail, healthcare, advertising, finance, and logistics.
Anyone can benefit from the GICSEH Data Analytics Course, regardless of whether they are new to the industry, changing careers, or trying to improve their current abilities. A recent graduate can enter this program with skills suitable for the workforce and perform fantastic work as a data analyst because it is open to graduates from any field, including science, commerce, and the arts. It is a great training for individuals who have no experience with analytics or data-centric work and are moving into a new career that focuses on data and analytics. One of the highest-paying occupations for recent grads is data, and positions in this field offer employment security that many other positions would not.
Even if the position does not always have "data analyst" in the title, it can be a wonderful focus for your decision-making if you are interested in working in data analytics as a new role for your career or a new function in your career path. There is data everywhere, and its use in business is only growing. You will gain analytics skills that will make you a very desirable candidate for any position involving data or, maybe more crucially, sophisticated analytics for making decisions!
Entrepreneurs or business owners who wish to make data-driven decisions for the expansion of their enterprises would also find this training to be highly pertinent. Being able to organize, evaluate, and understand data that customers can use to take action can be a valuable skill for consultants or freelancers working with clients in any area of strategy, marketing, or business intelligence.
This course is very beneficial if you are a postgraduate student or researcher looking to add information that is pertinent to your studies or if you are becoming more aware of the worth of your academic credentials in order to build your ability to answer real-world issues in fields that are in high demand.
Employers are no longer only searching for IT expertise because AI, machine learning, IoT, and automation are profoundly altering the volume and complexity of data. Businesses are looking for data-literate people who can effectively clean, process, analyze, and visualize data. Nowadays, having a solid foundation in important technologies (such as Python, SQL, Excel, Tableau, and Power BI) and combining those skills with statistical knowledge and critical thinking are crucial for any career that is looking to the future. Students who enroll in a data analytics course can gain the foundational knowledge that has been shown to increase their employability and improve their future prospects.
Let's examine data analytics through the work of data analysts. To find patterns, trends, and insights that assist businesses in making better decisions, a data analyst gathers, purifies, arranges, and analyses vast amounts of unprocessed data. The term "data analytics" describes the utilization of unprocessed data after it has been cleaned, transformed, and modelled in order to produce insights that can inform choices. The process of analysing big and complicated datasets using a variety of tools and approaches to extract pertinent meaning in the form of patterns, trends, and correlations is known as data analytics.
In numerous industries, companies use data analytics to boost productivity, optimize operations, make better decisions, and gain a competitive edge. In order to get insights from data, the multidisciplinary area of data analytics combines statistics, mathematics, computer science, and domain experience. By mastering the skills provided by the data analytics course in India, you will be able to recognize trends and patterns that may have an impact on the future and transform raw data into actionable insight.
Various types of Data Analytics
There are 4 major types of Data Analytics we will talk about :
1.The first type of Data Analytics is Predictive Analytics
The data is transformed into useful, actionable knowledge through predictive analytics. Data is used in predictive analytics to determine the likelihood of a condition developing or the probable result of an event. A range of statistical methods, including modelling, machine learning, data mining, and game theory, are used in predictive analytics to examine past and present data and forecast future events.
Predictive analytics uses the following techniques:
Linear Regression, Data mining, time series analysis, and forecasting
Predictive modelling, decision analysis and optimisation, and transaction profiling are the fundamental pillars of predictive analytics.
2. The second type of Data Analytics is Descriptive Analytics
In order to get insight into how to approach future events, descriptive analytics examines data and analyses prior events. It examines past performance and analyses it by analysing historical data to determine what led to previous success or failure. This kind of analysis is used in practically all management reporting, including finance, operations, marketing, and sales.
In order to categorise clients or prospects into groups, the descriptive model quantifies the relationships in the data. Descriptive analytics finds a wide range of customer-product interactions, in contrast to a predictive model that concentrates on forecasting the behaviour of a specific consumer.
Descriptive analytics is commonly used in company reports that incorporate historical reviews, such as data dashboards, reports, descriptive statistics, and data queries.
3. The third type of Data Analytics is Prescriptive Analytics
Prescriptive analytics automatically combines machine learning, business rules, mathematical science, and big data to provide a prediction. It then recommends a course of action to capitalise on the prediction.
Beyond just forecasting future events, prescriptive analytics also suggests actions based on the forecasts and illustrates the implications of each option for the decision-maker. Prescriptive analytics predicts not just what will occur, when it will occur, but also why. Additionally, Prescriptive Analytics can outline the implications of each selection alternative and offer suggestions for how to seize a future opportunity or reduce a future risk.
Prescriptive analytics, for instance, can help with strategic planning in the healthcare industry by utilising analytics to harness operational and consumption data along with data from external sources like population demographics and economic statistics.
The fourth type of Data Analytics is Diagnostic Analytics
To answer any query or solve any problem, we typically employ historical data in this study rather than other data. We look for any patterns or dependencies in the problem's previous data.
Companies utilise this analysis, for instance, because it provides valuable insight into an issue. They also maintain precise records regarding their disposal; otherwise, data collecting may prove to be time-consuming and unique to each problem.
The following methods are frequently employed in diagnostic analytics:
-Data mining, data discovery, and correlations.
Employers in India want to hire candidates who posses:
1. Outstanding proficiency in SQL for data extraction and manipulation.
2. Experience in Python and R programming for automation and data application.
3. The ability to visualise data using Tableau, Power BI, or Matplotlib to generate and share insights.
4. Understanding of the foundations of AI and machine learning to automate analytics data processes and build predictive models.
5. Knowledge of and experience with big data technologies, such Hadoop or Spark, which are used to handle massive volumes of data.
6. Excellent domain expertise and business fluency to enable data insights to more closely align with an organization's goals.
7. Excellent communication abilities and the ability to use data storytelling to explain complicated findings to stakeholders.
Companies from a variety of industries are looking for qualified individuals to analyse data and inform decisions, making data analytics one of the fastest-growing fields in India. The breadth and need for data analytics capabilities have increased further with the integration of digital technologies, artificial intelligence, and machine learning.
Why Pick a Data Analytics Career?
Strong Demand: To use data for strategic advantage, businesses in a variety of industries, including retail, healthcare, finance, and information technology, are actively hiring data analysts.
Profitable Pay : Entry-level positions pay between ₹4 and ₹6 lakhs annually, mid-level professionals ₹8 to 12 lakhs, and senior analysts or managers ₹20 lakhs or more.
Diverse Industries : Technology, finance, e-commerce, consulting, healthcare, and manufacturing are among the industries with opportunities.
Career Growth Opportunities
Vertical Progression: Move from junior analyst to senior roles, including Data Scientist, Data Engineer, or Analytics Manager.
Specialization: Different career paths have different levels of specialization; financial analytics, healthcare analytics, or marketing analytics are all promising fields with high demand and compensation.
Consultancy: With sustained relevant experience, data analysts can also pursue consultancy roles where they can provide data / analytics expertise across many industries with increased compensation.
Leadership: With a number of years of experience, data analysts can also pursue leadership opportunities where they could lead teams of other data analysts or take on data-centric strategic leadership roles, such as Chief Data Officer.
The process for admission at GICSEH
The Global Institute of Cyber Security & Ethical Hacking is structured in such a way that aspiring IT and cybersecurity professionals should find it relatively easy to understand and follow:
Eligibility : Most of GICSEH's certificate programmes and degrees, require candidates to have completed 12th grade or +2. For more advanced programmes, like Certified Ethical Hacking (CEH), the candidate should have either an advanced diploma in network security or an IT degree. Candidates must have fundamentals in networking, security standards, and operating systems.
Admission: GICSEH usually has a direct admission process for its programmes, meaning that candidates can apply to the institution without an admission test.
Complimentary counseling sessions: Prospective students should speak with a GICSEH experienced counselor to discuss the courses available, potential career prospects, and any other queries before they registered.
Application: Complete the application from any Campus or from the GICSEH website.
Fees: Abide by the fee schedule of the program. For instance, diploma programmes cost around ₹70,000 and run for around a year.
Timings: Classes generally start during the first or last week of the month. After commencing the programme, you will be assigned to a batch based on your requested date.
Live projects, practical labs, and placement assistance are all part of the training.
To start the admissions process, new applicants should get in touch with GICSEH admissions for individualised advice.
• Technical Skills: Proficiency in statistics, Excel, Tableau, R, Python, SQL, and Power BI.
• Analytical Thinking: The ability to analyze complex data and provide useful recommendations.
• Continuous Learning: Growth depends on keeping up with tools, technologies, and trends in the industry.
• Certifications: You can enhance your profile with courses from locations such as Coursera, edX, and LinkedIn Learning.
• Growing Market: A projected 25% increase in data analytics roles will exist in India by 2030, proving to be one of the most rapidly expanding sectors.
• Veracity with Artificial Intelligence / Machine Learning: The analytics sector will also become more combined with AI and ML fields, creating more opportunities and responsibilities.
• Real-Time Analytics Sector: As companies are moving towards real-time data analytics approaches, analysts will be expected to be prompt and knowledgeable regarding what they report as analysis.
• Data-Driven Culture: Businesses want and expect data to drive decisions at all levels or tiers of business, which opens up more opportunities for analytics specialists.
• Some large IT companies: Infosys, TCS and Wipro; consulting companies: Deloitte, KPMG; e-commerce giants: Flipkart and Amazon; banks: ICICI, HDFC, and Axis Bank.
• Tech startups and firms: For data analysts working in India, these soft skills also matter because they affect not only how they comprehend data, but also how they collaborate with other team members, communicate insights to other employees, and have business impact. While technical skills do matter, exceptional analysts are often differentiated in people-centred ways.
• Communication: It is essential to be able to convey complex data findings clearly and practically to both technical and non-technical stakeholders. It is also a strong presentation and data storytelling skill, which helps communicate findings in an engaging and accessible way.
• Collaboration: Data analysts are often found working in multidisciplinary teams. Successful projects rely on effective collaboration, receptiveness to feedback and work well with colleagues from diverse fields.
• Critical Thinking: Analysts must evaluate evidence in an unbiased manner, challenge assumptions and develop meaningful, evidence based conclusions. This helps to ensure that the insights are valid and valuable.
• Problem Solving: The job often involves identifying challenging or ambiguous business problems. Effective problem solving skills allow analysts to break problems down, identify root causes, and develop innovative solutions that are evidence based.
• Attention to Detail: There can be no compromise on accuracy. The highest level of care must go into data cleaning, validation, and documentation, as even the smallest errors made in the course of data analysis can lead to expensive errors.
• Flexibility: New tools and approaches are constantly being developed, and the data landscape is rapidly evolving. Flexibility and openness to new knowledge are important to continuous growth.
• Time management: Data analysts tend to have multiple projects and deadlines to manage, and proper time management and prioritising of work will help guarantee good quality and reliable results.
• Creativity: Analysts with creative thinking are more likely to identify trends, comprehend data, and develop unique and different solutions about problems.
• Work Ethic and Professionalism: In teams and organisations, trust and respect are fostered by dependability, integrity, and a strong sense of duty.
• Leadership (at higher positions): Leadership abilities are crucial for managing teams, guiding strategic initiatives, and coaching junior analysts as they progress.
Together with technical proficiency, mastering these soft skills enables data analysts in India to succeed, progress in their careers, and provide their companies with the most value possible.
For data analysts in India, soft skills are crucial since they influence not just how they understand data but also how they work with teams, share insights, and create business impact. Although technical proficiency is important, exceptional analysts are frequently distinguished from the others by these human-centered skills.
The field of data analytics is developing and has a promising future. With the rise of cloud computing, big data, machine learning, and artificial intelligence (AI), data analysts are becoming increasingly important. Analysts will be essential to businesses because they will not only assess previous events but also offer predicted insight into what actions companies should take in the future. As businesses use digital tools and automation, much more data will be produced. For those who can interpret all of this data, this leads to additional roles.
Any data analytics programme must include practical instruction and exposure to the real world, according to GICSEH India. Importantly, the programme aims to close the gap between theory and practice so that students have the knowledge and self-assurance to use it in a real-world business setting. Students will work through problems that are similar to those that experts at top-tier organisations are doing by using industry-standard tools and datasets through interactive workshops. Following a comprehensive explanation of the practical tasks that students will be performing, they include, among many others, the construction and training of neural networks, the execution of machine learning algorithms, and the most up-to-date techniques for data visualisation.
Students build a stronger employment portfolio that highlights their skills to prospective employers by gaining experience with real-world projects that will be combined throughout the project journey. Examples of these projects include analysing e-commerce sales trends, forecasting health outcomes, and spotting financial fraud.
At Global Institute of cyber security and ethical hacking (GICSEH) the mission is to educate talented and motivated individuals through quality.
Through workshops, internships, and real-world team projects, GICSEH also facilitates professional development and relationships by giving participants direct access to industry experts. The GICSEH experience is more than just a technical skill development training course. It is a chance for you to grow both personally and professionally. Every practical project you work on will help you develop your technical abilities while also boosting your self-esteem. Throughout the journey, you will also face cooperative tasks requiring resilience. You will encourage and cultivate your creative problem-solving skills for every group project.
Working with classmates and mentors who are as passionate about learning as you are can help you develop your communication and teamwork skills, which will enable you and your peers to achieve more. Taking advantage of the personal development goal for your heart and guts, you will be challenged emotionally, learn how to deal with real-world issues, and have the chance to make breakthroughs. In the ever-changing world of data, all of these experiences help you become a resourceful and solution-focused analyst.
By attending GICSEH you are investing in a positive future and engaging in an awesome community. Not only with mentors and peers who are invested in your growth, but one that will challenge your aspirations and help create opportunities to push the limits of your barriers. Every lesson is an opportunity to challenge yourself to think divergently, to explore your curiosity, and to innovate the possibilities towards your future ambitions with a purpose. The program provides deeper access for you to be the best version of you and sets you up to jump into your next best opportunity.
By enrolling at GICSEH, you are participating in an amazing community and making an investment in a bright future. Not just with peers and mentors who care about your development, but also with someone who will push your goals and help you find chances to break through your boundaries. Every lesson is a chance to push yourself to think creatively, to follow your curiosity, and to come up with new ideas for your future goals with a purpose. The programme helps you become the best version of yourself and prepares you to seize your next great chance.
Welcome to the GICSEH Data Science Course in India, an extensive, industry-aligned curriculum that prepares you for real-world data science challenges by combining fundamental theory with practical application.
DATA SCIENCE-
Data Science: First Lesson: Overview of Data Science (two hours)
An introduction to data science, its broad use, and its significance in the data-driven world of today are covered at the beginning of the course. Students will learn about data science across a variety of sectors, such as technology, healthcare, finance, and retail. The history of data science will also be covered in the workshop, starting with statistical analysis and progressing to the multidisciplinary field it is today.
Lesson 2: Four Hours of Statistics
The fundamental and significant statistical ideas covered in this session are essential for any data scientist. Probability distributions, which form the basis of data analysis, are taught to students. The differences between inferential statistics, which draw general conclusions from data, and descriptive statistics, which provide summaries of data sets, are covered in this lesson. Lastly, students are given the chance to use data to make decisions through an introduction to hypothesis testing that shows how to test significance.
Lesson 3: Visualisation of Information (three hours)
The fundamentals of data visualisation are covered in this session, including what visualisation is, why it matters, and how it uses graphical representation to assist people in identifying patterns in data. Students learn how to visualise distributions, correlations, and patterns in data while honing their ability to decipher data visualisations and use them as a tool. The session exposes students to popular tools and libraries including Matplotlib and Seaborn.
Lesson 4: Structures, Manipulation, and Data Mining (5 Hours)
The field of data mining is examined in this unit. The area of data mining focuses on techniques that draw valuable insights from vast amounts of data. The data mining method will be understood by the students, along with the ethical concerns and difficulties that arise during the process. Along with explaining how to handle and apply the data structures to succeed in data science, the lecture covers the main data structures in Python, including lists, tuples, dictionaries, and sets.
Lesson 5: Algorithms in Machine Learning (4 Hours)
Students study many of the different algorithms that power that field and are introduced to the foundations of machine learning. This lecture assists students in making the transition to practical algorithm implementation by outlining the distinctions between supervised and unsupervised learning approaches.
Lesson 6: Data Acquisition & The Data Science Life Cycle (4 Hours)
Students investigate a variety of data sources and discover the distinction between structured and unstructured data in this lesson. Students evaluate different data collection techniques as well as the meanings of database formats, spreadsheets, JSON, and CSV files. Additionally, the lecture outlines the key stages of the data science process and shows students how data scientists operate throughout the course of a project.
Lesson 7: Data Pre-processing (4 Hours)
To produce clean, high-quality datasets, data pre-processing is essential. A range of cleaning and pre-processing techniques will be covered in this lecture, with an emphasis on techniques for handling outliers and missing values. In order to build better models, learners will be introduced to feature engineering, which teaches them how to create new, more valuable qualities from existing data.
Lesson 8: Evaluation, Experimentation & Project Deployment Tools (6 hours)
The most popular data science tools and platforms, including RStudio, Anaconda, and Jupyter Notebook, are introduced to the students. Using a version control technology like git, this lesson introduces students to a crucial aspect of development and project management teamwork. Students will be given detailed instructions on how to set up a data science environment, including how to install and configure tools and libraries, depending on the context in which they will be working.
Lesson 9: Predictive Analytics - Segmentation using Clustering (4 hours)
The use of clustering techniques to group related data points according to their properties and functioning will be covered in this session. The focus will be on unsupervised clustering techniques, such as hierarchical clustering and k-means. Students will get the ability to assess the quality of clustering results. Students will gain some experience with predictive analytics in this topic, which uses sophisticated machine learning techniques to examine real-world datasets.
Lesson 10: Working on Real World Projects (4 hours)
Students will work on practical learning and development projects that apply the skills they have learnt across the modules, including data visualisation approaches. In order to solve real-world data difficulties, learners will base their projects on real-world datasets. Students will be encouraged to exchange discussion points, give and receive constructive criticism, and present the outcomes of their projects to their peers.
Lesson 11: Big Data Basics and Hadoop Integration with R (5 hours)
Students learn a theory about big data and why it is important in data science today in the last lesson. The properties of big data like volume, velocity, diversity, and veracity as well as the difficulties that come with it will be covered in the lesson. The Hadoop ecosystem, in particular the Hadoop Distributed File System (HDFS), and how it handles processing and storing massive volumes of data dispersed over numerous computers will also be covered for the students.
DATA ANALYTICS-
Welcome to the GICSEH Data Analytics Course, a comprehensive, industry-aligned training that blends essential theory with hands-on practice to prepare you for real-world data science challenges.
First Lesson: Overview of Data Analytics (one hour)
Students will be exposed to the concepts and vocabulary that will enable them to analyse data effectively. These guidelines will provide students with a strong basis for all subsequent course material. Along with exposing students to the digital resources and tools they might come across as they advance in the organisational micro-analytics course, this class will also look at the various software and applications that are most frequently used in the data area.
Introducing the R Programming Language in Lesson Two (one hour)
The syntax and structure of the R programming language will once more be explained to the learners. The RStudio IDE, possibly the most well-liked interface for R standard programming, will be introduced to students in this course. In addition to important control structures like loops and conditional expressions, topics covered include variables, variable types, and basic R functions. After completing this session, students will be able to write and execute simple R programmes.
Lesson 3: R Basics (5 Hours)
Students are exposed to new data structures or types of data, such as vectors, matrices, arrays, and data frames, building on their prior knowledge of R. Students have hands-on experience altering and accessing data by working within these structures, which prepares them to use data structures effectively in their analyses. The session also teaches students how to add functions to these data structures, which is a crucial ability that gives them practical experience with data transformation and summarization.
Lesson 4: R Packages (2 Hours)
This lecture introduces students to some of the robust data manipulation and analysis capabilities available in the R environment. Students learn how to manipulate data using the dplyr package and how to reshape and tidy datasets using the tidyr tool. This helps students work more effectively with complicated and large datasets by introducing them to methods for efficiently filtering, choosing, organising, and summarising data.
Lesson 5: Data Importation (2 Hours)
In order to perform analytics in practice, students will learn how to import and export data from various sources into R. We will learn how to read and write data from databases and other file types in this course through hands-on practice. Students will learn how to retrieve the data they require for a variety of analytical tasks and situations by honing these abilities.
Sixth Lesson: Data Manipulation (3 Hours)
Advanced R data manipulation techniques are covered in this lecture. It covers an overview of text mining and natural language processing (NLP), as well as forecasting through the manipulation of time-series data. In order to help students increase their technical abilities, this session also explains how R can be used with various tools in more complex analytical applications.
Lesson 7: Error Metrics (3 Hours)
By using specific data cleaning procedures to identify and correct problems, students concentrate on maintaining the quality of the data. In order to prepare datasets for analysis, this lecture first focuses on how to handle data by category before introducing encoding and transformation techniques. Analytical results will be more robust and dependable if these practices are followed.
Lesson 8: Machine Learning (5 Hours)
This course specifically examines a branch of artificial intelligence (AI). The course covers the development of the AI industry and its continuous growth into many different industries. Along with the ethical implications and societal concerns of AI, the fundamental ideas of machine learning are explored. Students understand the differences between supervised, unsupervised, and reinforcement learning in addition to being introduced to important algorithms like linear regression, decision trees, and k-nearest neighbours.
Lesson 9: Supervised Learning (6 Hours)
As students learn the concepts and goals of this branch of machine learning, this session gives them a greater understanding of supervised learning. Students obtain practical tools for typical machine learning tasks by gaining hands-on experience building classification algorithms for categorical task prediction and regression methods for continuous result prediction.
Lesson 10: Unsupervised Learning (4 Hours)
Unsupervised learning techniques like k-means clustering and hierarchical clustering are the main topic of this course. Along with practical frameworks for applying unsupervised learning to explore fresh data and search for underlying patterns in data when there are no labelled outcomes, students are also introduced to dimensionality reduction techniques.
Lesson 11: Natural Language Processing (7 Hours)
Students think about how human language and machines can communicate. They go over fundamental NLP ideas and how they apply to different sectors like customer service, healthcare, and finance. The lesson covers gathering and collecting textual material, doing basic cleaning, and progressing to more complex lessons in natural language processing, including conversational AI and question answering. Through projects, students are also given the chance to gain practical experience with the newest advancements in NLP, such as big language models.
Lesson 12: Deep Learning (6 hours)
An overview of deep learning and neural networks is given in this last session, along with information on their historical evolution and current applications. Artificial neural networks, their designs, and their primary methods (feed-forward and backpropagation) are examples of core principles. Students learn about popular frameworks (PyTorch and TensorFlow), engage in practical exercises where they construct and train their own neural networks, and end by weighing the advantages and disadvantages of different deep learning tools.