Data Analytics Course in Delhi, Data Analytics Training Institute in Delhi

Data Analytics Course In Delhi

Rating on Best Python Programming Training Institute & Certification in Noida

4.9 out of 5 based on 4000 Students Rating

About Course
Course Outline

Course Summary

Syllabus of Data Analytics and Data Science

Data Science:

Lesson 1 – Introduction to Data Science (Duration-2 Hours)

• 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-4 Hours)

• Overview of basic statistical concepts
• Understanding to probability distributions
• Differentiation between descriptive & inferential statistics
• Introduction to hypothesis testing methodologies and significance levels

Lesson 3 - Information Visualisation (Duration-3 Hours)

• 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-5 Hours)

• 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-4 Hours)

• Introduction to machine learning & its algorithms
• Implementing supervised and unsupervised learning techniques

Lesson 6 - Data Acquisition & Data Science Life Cycle (Duration-4 Hours)

• 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-4 Hours)

• 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-6 Hours)

• 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-4 Hours)

• 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-4 Hours)

• 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-5 Hours)

• 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.

Data Analytics:

Lesson 1 - Introduction to Data Analytics (Duration-1 Hour)

• 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-1 Hour)

• 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-5 Hours)

• 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-2 Hours)

• 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-2 Hours)

• 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-3 Hours)

• 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-3 Hours)

• Implementing data cleaning methodologies to fix errors.
• Strategizing category wise data handling through encoding and transformation techniques.

Lesson 8 - Machine Learning (Duration-5 Hours)

• 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-6 Hours)

• 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-4 Hours)

• 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-7 Hours)

• 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 Analytics Training in Delhi

4.9 out of 5 based on 4000 Students Rating

Rising Demand for Data Analysts

The demand for a data analyst in the fast-paced world has become more pronounced for organizations. Now, organisations also realise the importance of data analysis to make better decisions to pursue a business successfully. The demand for data analysts in tech hub Delhi is surely increasing. A Data Analytics Course in Delhi might be the best course for someone who wants to start a well-rewarded career in data analytics. Data Analytics has generated the importance of data management where information has become a form of wealth employed by companies to earn a competitive edge. In this respect, data analysts facilitate insightful information for the success of the organisation. Additionally, addressing the metropolitan city. Delhi is a tech hub that has witnessed an upward trend in demand for data analytics professionals. Most organizations are investing heavily in advanced technologies; hence, there’s a need for expert data analysts who can help the organization with data for a good business. To enrol yourself in Data Analytics Course in Delhi, choose GICSEH, which is gaining popularity due to its ability to provide candidates with the knowledge and necessary tools to help them grab a successful career in Data Analytics.

Importance of Data Analysts

Data Analytics is essential in today’s data-driven world. It allows organizations to extract meaningful insights from vast amounts of information, enabling smarter, faster, and more accurate decision-making. Whether it's understanding customer preferences, forecasting sales, or optimizing internal operations, data analytics helps improve efficiency and performance. Businesses that invest in analytics gain a competitive edge by responding to market trends and customer needs more effectively. In every industry from retail to healthcare data analytics is now a fundamental part of strategy, operations, and growth. Learning these skills today means preparing for tomorrow’s most in-demand careers.

Data analysts play an important role in businesses today. Thus, the core role of a data analyst is to collect, process, and present the data in a significant manner with useful information to be used in running a successful business. Data can also be used to make strategic decisions to outsmart the level of your competitors. Data informs concerning strategic decision-making. It also helps in improving operational efficiency, enhancing the experience of customers, and making an informed decision. From e-commerce, to finance, and even manufacturing, with all these technological advancements, every industry will now need data analysts. The future role of Data Analytics will continue to expand and stand out in generating informational strategies for business.

Impact of Data Analysts

In today’s world the impact a data analyst plays is highly transformative. It empowers businesses, governments, and organizations to make decisions backed by evidence instead of intuition. In e-commerce, analytics personalizes customer experiences and boosts sales. In healthcare, it improves patient care by predicting disease outbreaks and treatment effectiveness. In finance, it prevents fraud and drives smarter investments. Even education, logistics, and sports are optimizing outcomes using analytics.

Data-driven insights lead to better resource allocation, cost savings, and strategic planning. With artificial intelligence and big data technologies advancing rapidly, the role of analytics continues to grow. It’s no longer a luxury—it’s a necessity. For professionals and students, mastering data analytics means unlocking high-paying, future-ready careers across industries.

GICSEH – Your Pathway to a Data Analytics Career

Courses offered in the Data Analytics Training Institute 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 analytic training to help users gain hands-on experience with the tools and techniques available to combat real-world problems, including that of machine learning.

Benefits of Joining a Data Analytics Course in Delhi

Comprehensive Learning with Real-World Exposure

Joining a Data Analytics Course in Delhi has numerous benefits for users who wish to have a fulfilling career. Such courses will give users comprehensive knowledge about Data Analytics principles, starting from how to collect data to the most advanced analytical techniques. Another benefit to pursuing studies in the Data Analytics Course in Delhi is the networking. A good institute will connect its students with industry leaders, and take them to various workshops, internships, and many more. So, GICSEH can be the best institute for anyone who wants to grab the competitive job market just by mastering data analytics.

Expert Mentorship, Tools Training & Placement Support

The Data Analytics Course at GICSEH doesn’t just provide technical education to you. The students have the opportunity to work on real-world projects using retail, healthcare and finance data sets. The job-oriented courses focuses on employable skills and market needs. The students get to walk into a occupation. The students have hands-on experience on industry-standard tools like Excel, SQL, Python, Tableau, Power BI, and the fundamentals of machine learning. Students functionality with tools is governed and mentored by certified trainers. The institutes' wide network allows consistent access to internship opportunities and related work experience with 100% placement support. Lab hours available 24x7 along with LMS support, students have a choice of offline, online, hybrid class schedules, and a recognized certification in the industry for data analytics, all of which meet the needs of the students.

Development of Soft Skills for Career Readiness

In addition to technical mastery, the course also emphasizes essential soft skills that make a real difference in the workplace. The learners are trained to communicate effectively and provide insight to both technical and non-technical types of audiences. Learners are encouraged to think critically and be creative problem-solvers in order to view business situations from different angles. They participate in team and individual projects, which improves their teamwork and collaboration. Learners are also trained to produce dashboards and presentations to provide visual stories through complex data. This is a unique tool and will expand their capability to provide data stories. Finally, they engage in time management training so they can handle more than one project and prepare for the responsibilities of a modern-day, data-driven professional role.

What is Data Analytics?

Let’s understand Data Analytics by what Data Analysts do. A data analyst collects, cleans, organizes, and interprets large volumes of raw data to uncover patterns, trends, and insights that help businesses make smarter decisions. Data analytics refers to the the exploitation of raw data, which we complete the process of cleaning, transforming, and modeling, to generate insights from the data that can guide decision making. Data analytics is a process that involves many tools and techniques to analyze large and complex datasets in order to extract relevant meaning in formats such as patterns, trends, and correlations. This field covers a wide array of methods, such as descriptive analytics, which summarizes and describes data; diagnostic analytics, which digs into the reasons behind past events; predictive analytics, which looks ahead to forecast future trends; and prescriptive analytics, which suggests actions based on the analysis results.

Organizations utilize data analytics to improve their decision-making, optimize operations, increase efficiency, and create a competitive advantage in many industries. Data analytics is a multidisciplinary field that integrates statistics, mathematics, computer science and domain expertise to extract insights from data.In learning the skills offered through the data analytics course in Delhi, you will master the ability to turn raw data into actionable intelligence and identify trends and patterns that can influence the future.

Types of Data Analytics

Descriptive Analytics: Discovering What Happened

Descriptive analytics helps to answer the question "What happened?"

It is a way of taking historical data and aggregating it in many different way to create charts and tables. Examples would include sales from last month, website visits for some period, or aggregate customer feedback. It allows businesses to understand what happened in the past and see any trends that occurred in that data.

Diagnostic Analytics: Finding Out Why

After knowing what happened, you can now ask "Why did it happen?"

Diagnostic analytics goes one step further. This kind of analytics looks for relationships and patterns, and uses that information to explain why one outcome occurred instead of a different outcome. If your website saw a significant decrease in traffic, for instance, one could use diagnostic analytic tools to determine if the decrease was because of slow loading speed, SEO changes in your market, or the performance of a marketing campaign.

Predictive Analytics: Forecasting Future Events

This is where we transition from understanding the data to framing insights within anticipatory context. Predictive analytics uses information from diagnostic analysis, applies statistical and machine learning models, and uses that output to explain: "What could happen next?"

Businesses utilize predictive analytics to forecast sales, predict customer churn, or anticipate demand patterns in the market which helps them stay ahead of their competition.

Data Analytics Institute in Delhi