At 4Achievers, we offer a comprehensive Data Science Training course in Chandigarh that is designed to equip students with the skills and knowledge needed to excel in the rapidly growing field of data science. As the leading Data Science Training institute in Chandigarh, our course is designed for individuals looking to gain hands-on experience in data analysis, machine learning, deep learning, and big data technologies.
Our training program goes beyond theory, focusing on practical learning through live projects and real-world applications. With expert trainers, state-of-the-art IT labs, and placement assistance, we ensure that each student is prepared for a successful career in data science.
Feature | Details |
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Comprehensive Curriculum | Our curriculum covers all essential topics, including Python, R, machine learning, data visualization, and big data tools (Hadoop, Spark). |
Hands-On Learning | Students work on live data science projects to apply theoretical knowledge and gain real-world experience. |
Expert Trainers | Learn from industry experts who have practical experience in data science and machine learning. |
Placement Assistance | We provide job placement support, including access to our network of recruiters and interview preparation. |
State-of-the-Art Labs | Our IT labs are equipped with the latest tools, including Python, R, Tableau, and Hadoop, to ensure students are well-prepared for the industry. |
At 4Achievers, our students get the opportunity to work on live projects that simulate real-world business problems. These projects are an essential part of the training, providing valuable practical exposure. Students work with actual datasets, perform data cleaning, build machine learning models, and present data-driven insights to stakeholders.
Live Project | Description |
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Data Preprocessing | Students work on cleaning, transforming, and organizing raw data into a usable format for analysis. |
Machine Learning Model Development | Students build and evaluate machine learning models (e.g., classification, regression) using Python and R. |
Data Visualization | Students create visualizations and dashboards to present data insights using tools like Tableau or Matplotlib. |
Big Data Analytics | Students work on analyzing large datasets using Hadoop and Spark, applying their skills to real-world big data problems. |
One of the core offerings of 4Achievers is our placement assistance. We understand the importance of a successful career in data science, and our dedicated team helps students secure jobs in top companies. From resume building to mock interviews, we provide all the necessary support to ensure students are prepared for the job market.
Placement Support | Details |
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Resume Building | Tailored resume writing assistance to highlight your data science skills and achievements. |
Mock Interviews | Regular mock interview sessions to help students gain confidence and perform well in real interviews. |
Job Referrals | Access to our vast network of hiring companies in tech, finance, healthcare, and more. |
Career Counseling | Personalized career counseling to guide you through career choices, certifications, and interview preparation. |
At 4Achievers, we believe that access to advanced tools and technologies is essential for effective learning. Our IT labs are equipped with the latest software and tools used in the data science industry. Students get hands-on experience working with platforms such as Python, R, Hadoop, and Tableau, ensuring they are well-prepared for the job market.
Lab Facility | Tools & Software Available |
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Data Science Software | Python, R, Jupyter Notebooks, and more for building data models and analysis. |
Big Data Tools | Hadoop, Spark, and Apache Kafka for processing and analyzing large datasets. |
Data Visualization Tools | Tableau, Power BI, and Matplotlib for creating interactive dashboards and visual reports. |
Machine Learning Tools | Scikit-learn, TensorFlow, and Keras for building and evaluating machine learning models. |
At 4Achievers, we are committed to providing the highest quality of training to students looking to pursue a career in data science. Our comprehensive curriculum, hands-on learning approach, expert mentorship, and placement support make us one of the best Data Science Training institutes in Chandigarh.
At 4Achievers, one of the leading Data Science Training institutes in Chandigarh, we integrate Agile methodologies into our Data Science Training to provide an interactive, hands-on learning experience. Agile is a flexible, iterative approach to project management that focuses on collaboration, continuous improvement, and delivering value quickly. By incorporating Agile activities into our training, we ensure that our students not only learn data science concepts but also understand how to work efficiently in a team-oriented, real-world environment.
Agile activities help students build practical skills by simulating real-world data science projects where they can apply their learning step-by-step, gather feedback, and continuously improve. This approach allows students to experience the same methodologies and strategies they would use in actual data science teams or companies.
In Agile, Sprint Planning helps students break down their training into manageable, focused tasks that can be accomplished in short periods. At 4Achievers, our Data Science Training includes setting clear goals for each sprint, where students focus on specific data science tasks like data cleaning, feature engineering, or building machine learning models.
Key Tasks in Sprint Planning:
Sprint Activity | Description |
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Data Collection | Collect and preprocess raw data to make it ready for analysis. |
Model Building | Build a machine learning model using algorithms like Linear Regression or Decision Trees. |
Feature Engineering | Create and select important features from the dataset to improve model accuracy. |
In Agile, daily standups are short meetings where team members discuss their progress, challenges, and plans for the day. During Data Science Training at 4Achievers, daily standups allow students to discuss what they accomplished the previous day, the challenges they are facing with their models, and what tasks they will focus on next.
Daily Standup Goals:
Standup Focus | Description |
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Data Exploration | Discuss findings from exploratory data analysis (EDA). |
Model Tuning | Talk about how to improve model accuracy or efficiency. |
Collaboration | Discuss data science challenges with peers and mentors to find solutions. |
After each sprint, students at 4Achievers present the work they’ve done during the sprint. This review process allows students to show their progress, demonstrate model results, and receive constructive feedback from peers and mentors.
Key Elements of Sprint Review:
Sprint Review Activity | Description |
---|---|
Model Performance Review | Present the results of a model, including its metrics and performance. |
Data Insights Presentation | Share important insights or trends identified from the dataset. |
Feedback and Improvements | Gather feedback to improve data models or enhance data analysis. |
The Retrospective is a crucial Agile activity where students reflect on their process and identify what worked well and what could be improved. This activity encourages continuous learning and ensures that each sprint is more efficient than the last.
During the Data Science Training at 4Achievers, the retrospective provides students the opportunity to look back at the sprint to identify challenges faced, how they solved them, and areas where they can improve in future sprints.
Focus Areas in Sprint Retrospective:
Retrospective Activity | Purpose |
---|---|
Process Evaluation | Review how well Agile processes were followed, identifying areas for improvement. |
Challenge Resolution | Reflect on issues faced during the sprint and discuss how to overcome them in future sprints. |
Goal Setting for Next Sprint | Based on the review and feedback, set specific goals and objectives for the next sprint. |
The Agile framework used during Data Science Training in Chandigarh provides students with multiple advantages, such as improved teamwork, faster learning, and real-world problem-solving abilities. Here are some of the core benefits:
Benefit | Description |
---|---|
Real-World Experience | Students get hands-on experience with live data science projects, preparing them for the industry. |
Team Collaboration | Agile fosters strong teamwork, which is essential in data science projects where collaboration is key. |
Faster Problem Solving | Working in short sprints allows students to focus on one issue at a time, find solutions, and improve quickly. |
Continuous Improvement | Regular feedback loops ensure that students are constantly improving their skills and output. |
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We think that knowledge is power, and we want to give you the tools you need to achieve your goals. So be sure to check out our website frequently for the latest Course Program and Syllabus!
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This Data Science course near me is designed to give you a comprehensive understanding of the fundamentals of data science and the tools and techniques used to analyze data. You will learn the basics of data analysis, data visualization, machine learning, and more. You will also gain hands-on experience with popular data science tools such as Python, R, and SQL.
Data visualization is an essential tool in data science as it allows data to be presented in an easily understandable format. Data visualization can take many forms, including graphs, charts, and maps, and is used to help people better understand the data and draw insights from it. Data visualization can help to identify patterns, trends, and outliers in data that would otherwise be difficult to identify. 4Achievers can also help to communicate data to a wider audience, as visual representations are often easier to interpret than complex numbers and text. Data visualization can also be used to test hypotheses and explore possible relationships between variables, helping to further our understanding of data. Ultimately, data visualization is an invaluable tool in data science, as it helps to make data more understandable and accessible.
Data mining and analytics are used to gain insights into patterns and trends that may exist within large sets of data. By analyzing data, organizations can uncover meaningful information that can help them make more informed decisions and optimize their operations. Data mining and analytics can be used to identify correlations between different variables, uncover customer preferences, identify customer segments, detect fraud, forecast trends, and more. Ultimately, data mining and analytics can help organizations gain a better understanding of their data and make better decisions that can improve their operational performance.
Data mining is the process of uncovering patterns in large datasets. 4Achievers involves sorting through and analyzing large amounts of data to identify trends and correlations that can be used to inform business decisions. Database querying is the process of retrieving information from a database. 4Achievers involves requesting data from a database using a query language such as Structured Query Language (SQL). 4Achievers query language is used to specify which data to retrieve and how to format it. 4Achievers output of the query is the requested data. 4Achievers main difference between data mining and database querying is that data mining is used to uncover patterns in large datasets while database querying is used to retrieve specific information from a database.
Data cleaning is an essential step in data science that involves preparing and organizing data for analysis. 4Achievers involves identifying and correcting inaccurate, incomplete, or irrelevant data elements and transforming them into a usable format. Data cleaning helps ensure data accuracy, validity, and completeness, which are necessary for effective analysis. 4Achievers also helps to remove any inconsistencies or outliers from the data set. Data cleaning is important for producing reliable and meaningful results from data analysis.
Predictive analytics is a vital part of data science that helps in uncovering insights from data and predicting future outcomes. 4Achievers enables data scientists to identify patterns and trends in large data sets and use them to make informed decisions. Predictive analytics helps in predicting customer behavior, forecasting financial markets, and optimizing inventory control. 4Achievers is also used for predicting customer churn, fraud detection, and sales forecasting. Predictive analytics has become increasingly important in data science due to its ability to identify patterns, trends, and correlations in data that can be used to make decisions. Additionally, predictive analytics can help optimize operations and automate decision-making processes. By utilizing predictive analytics, data scientists can better understand customer behavior and make more informed decisions.
Data wrangling is the process of transforming and mapping data from one raw format into another format that is more useful for analysis. 4Achievers involves cleaning, structuring, and enriching raw data. This process can involve gathering data from multiple sources, changing the data into a format more suitable for analysis, and then exploring and manipulating data to uncover patterns and insights. Data wrangling is an iterative process and requires manual effort to identify and clean up data that is incomplete, inconsistent, or incorrect.
Data pre-processing is the process of preparing raw data for further analysis. 4Achievers includes cleaning and transforming data to make it ready for analysis, removing unnecessary and redundant data, and formatting the data for further use. Data pre-processing also involves feature engineering, where data is transformed and combined with other data sources to create new features that can be used for analysis. Data pre-processing is important because it helps the data to be more easily understood by computer algorithms and reduces the complexity of data analysis.
Data security is essential in data science because it ensures that sensitive data is kept secure and is not accessed by unauthorized individuals. Without proper data security, confidential data can easily be stolen or manipulated, leading to significant financial and reputational losses. Data security also helps to protect data from potential breaches and cyber attacks, which can have far-reaching consequences in the digital age.
Data security helps to protect data from accidental or malicious corruption, and ensures that data is available in its original form. Data security can also prevent data leakage, which can result in the loss of valuable intellectual property. Additionally, data security helps to maintain the accuracy and integrity of data, preventing unauthorized changes or manipulation of data.
Data security also helps to protect data from being shared without permission, or used for nefarious purposes. Data security measures help to ensure that data is used only for its intended purpose, and that it is not exposed to unauthorised individuals or organisations. Data security also helps to protect data from being misused by malicious actors, who may use it for financial gain or other malicious intentions.
Overall, data security is essential for data science because it helps to protect valuable data from malicious actors and ensures its accuracy and integrity. Data security also helps to protect data from being used for unintended purposes, and helps to ensure that data is used responsibly.
Artificial Intelligence (AI) is a rapidly growing field of data science that uses algorithms and computer systems to simulate human intelligence and behavior. AI can be used to automate, optimize, and analyze data in various ways. AI can be used to identify trends in data, make predictions, and detect anomalies. 4Achievers can also be used to identify patterns and correlations in data that may not be immediately obvious to humans. AI can be used to make decisions based on data, and to develop complex models that use data to predict outcomes and make decisions. AI can also be used to create self-improving systems that continuously learn from data and adapt to changing conditions. AI has already had a profound impact on data science and is likely to continue to be an important tool for data scientists in the future.
Statistics provide the foundation for data science by helping to summarize, analyze and interpret data. Statistics allow us to infer conclusions from data and make predictions about future trends. Statistics is also used to develop models and algorithms that can be used to make sense of large amounts of data. Statistical methods are used to analyze data from experiments, surveys and observational studies. Data science utilizes the tools of statistics to draw meaningful conclusions from data sets. Statistics provide the tools to address issues such as correlation, causation and the impact of various factors. Statistical techniques such as regression analysis, clustering, and machine learning help to uncover patterns and relationships within data sets. Data science also relies heavily on visualization techniques such as plotting and graphing to display data in user-friendly formats. Ultimately, statistics provide data science practitioners with the tools they need to turn data into actionable insights.
Data wrangling is the process of cleaning and organizing data to make it easier to analyze. 4Achievers involves sorting through data, correcting errors, and extracting useful information. Data wrangling often involves selecting, merging, and reshaping data sets to fit the requirements of a particular analysis.
Data transformation is the process of transforming data from one format to another. 4Achievers involves the conversion of raw data into a format that is more suitable for analysis. 4Achievers also involves the transformation of information to a form that can be used to create meaningful visuals or insights. Data transformation is often used to ensure data quality and enable data integration. Data transformation can involve such processes as data cleansing, data mapping, data aggregation, data integration, and data migration.
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First touchpoint for customer Initial handling of all customer tickets Track to closure of customer tickets by assisting the responsible teams System software and AWS/Azure infrastructure L1/L2 support Newgen solution / application L1/L2 support Responsib
Experience: 0 to 4 years Qualification:B.SC, B.Tech/BE/MCA Skills in one or more of JavaScript,CSS, Web application framework viz. Sencha EXT JS, JQuery etc., Delphi,C,C++,or Java..net,testing Cloud Administrator-managing Windows based Servers
Developing and deploying new applications on the windows azure PAAS platform using C#, .net core . Participation in the creation and management of databases like SQL server and MySQL Understanding of data storage technology (RDBMS, NO SQL). Manage applica
Experience of Dev Ops technologies, architectures and processes for 3 yrs Cloud Infrastructures Solutions: AWS EC2 ECS, S3 Cloudfront, RDS, Spot Instances, VPC, IAM, Security Groups, ELB etc), GCP, CI/CD Jenkins Containerization: Docker, Kubernetes System
Must have good knowledge of Google Cloud (GCP), Good To Have- AWS and. Azure Cloud automation, with overall cloud computing experience. Good knowledge of Windows Server and IIS (Internet Information Services). Good knowledge of .NET applications (ASP.Net,
Good Knowledge in both Manual Testing and Automation Testing,Strong experience in writing test scenarios and test cases Strong knowledge on Selenium, Appium, Microsoft SQL and Jmeter Adept in functional testing and reporting defects
Design, execute and report software tests, Review business / software requirements and provide inputs. Prepare test cases, test scripts and test data., Execute tests (manual / automated). Report defects and assist in their understanding., Analyse test re
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