MEC 101 Microeconomic Theory and Analysis| Latest Solved Assignment of IGNOU 
MEC 104 Economics of Growth and Development| Latest Solved Assignment of IGNOU 
MEC 102 Macroeconomic Theory and Analysis| Latest Solved Assignment of IGNOU 
MEC 109 Research Methods in Economics| Latest Solved Assignment of IGNOU 
MEC 106 Public Economics| Latest Solved Assignment of IGNOU 
MEC 107 International Trade and Development| Latest Solved Assignment of IGNOU 
MEC 110 Money,Financial Institutions and Markets| Latest Solved Assignment of IGNOU 
MECE 101 Introductory Econometric Methods| Latest Solved Assignment of IGNOU 
MECE 102 Advanced Econometric Methods| Latest Solved Assignment of IGNOU 
MCS 224 Artificial Intelligence and Machine Learing (From MCA Programme of IGNOU)| Latest Solved Assignment of IGNOU 
MGG 011 Geographical Information Systems-I (From MA Geography Programme)| Latest Solved Assignment of IGNOU 
MECE 104 Economics of Environment and Social Sector| Latest Solved Assignment of IGNOU 
MECE 103 Actuarial Economics| Latest Solved Assignment of IGNOU 
MGPE 6 Gandhi‘s Economic Thoughts| Latest Solved Assignment of IGNOU 
MWG 011 Women in the Economy| Latest Solved Assignment of IGNOU 
MGSE 009 Gender issues in Work, employment and productivity| Latest Solved Assignment of IGNOU 
MEC 103 Quantitative Methods | Latest Solved Assignment of IGNOU 
MEC 105 Indian Economic Policy | Latest Solved Assignment of IGNOU MCS 226 Data Science and Big...
Rs.299 Rs.599
| Product Name | Cart |
|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| Category | : MASTER‘S DEGREE PROGRAMMES |
| Sub Category | : Master of Arts (Economics)(MAEC) |
| Products Code | : 7.21-MAEC-ASSI |
| HSN Code | : 490110 |
| Language | : English, Hindi |
| Author | : BMAP EDUSERVICES PVT LTD |
| Publisher | : BMAP EDUSERVICES PVT LTD |
| University | : IGNOU (Indira Gandhi National Open University) |
| Pages | : 20-25 |
| Weight | : 157gms |
| Dimensions | : 21.0 x 29.7 cm (A4 Size Pages) |
The MCS 226 Data Science and Big Data assignment solution provides an in-depth study of data science concepts, big data technologies, and advanced analytics techniques used to handle and analyze large datasets. Aligned with IGNOU’s MCA programme guidelines, this solution focuses on the tools and methodologies used to derive actionable insights from vast amounts of data, enabling organizations to make data-driven decisions. It also covers how big data and machine learning play a crucial role in various fields like business analytics, healthcare, and scientific research.
The study begins with an introduction to data science, explaining it as an interdisciplinary field that combines statistics, data analysis, and computer science to extract valuable insights from structured and unstructured data. The solution emphasizes the importance of data science in today's digital world, where vast amounts of data are generated every second, and how data scientists use statistical modeling, data visualization, and machine learning to make sense of these large datasets. The solution explores the different stages of a data science project, including data collection, cleaning, exploration, and analysis, and explains how to use data visualization tools to present findings effectively.
The solution then delves into big data and the technologies that enable the processing and analysis of vast amounts of information. The study introduces key big data concepts, including volume, velocity, variety, and veracity, which define the challenges of big data. The solution covers the Hadoop ecosystem, a framework that allows for the distributed processing of large datasets across clusters of computers, and Apache Spark, which is a powerful engine for big data processing that can handle both batch and real-time data. The study highlights the advantages of NoSQL databases, such as MongoDB, Cassandra, and HBase, which are designed to store and manage unstructured data efficiently, and contrasts them with traditional relational databases.
The solution also covers data processing techniques, such as map-reduce, which is a programming model used in Hadoop for processing large-scale data in parallel across distributed systems. The study explores the significance of data mining in the context of big data, using algorithms to identify patterns, trends, and correlations within datasets.
The study continues with an exploration of machine learning techniques, which are crucial for making predictions and automating decision-making processes. The solution introduces supervised learning, where models are trained on labeled data, and unsupervised learning, where the algorithm identifies patterns in data without predefined labels. The study covers common machine learning algorithms, including linear regression, decision trees, k-means clustering, and support vector machines (SVMs), explaining how they are applied to various problems in data science, such as classification, regression, and clustering. The solution also discusses the importance of model evaluation and validation techniques, such as cross-validation, confusion matrices, and ROC curves, to assess the performance of machine learning models.
The solution then introduces deep learning, a subset of machine learning that uses neural networks to model complex patterns in large datasets. The study explains the architecture of artificial neural networks (ANNs), including feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), and explores their applications in fields such as image recognition, natural language processing (NLP), and speech recognition.
The study also covers important tools and programming languages used in data science and big data analytics. The solution explains how tools such as Python, R, and SQL are widely used for data manipulation, analysis, and visualization. The study highlights the importance of Python libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow, which provide powerful functionalities for data analysis and machine learning. It also introduces tools like Jupyter notebooks and Tableau for creating interactive data visualizations and reports.
For students seeking more personalized support, a custom handwritten option is available. This option allows students to receive tailored insights into specific aspects of data science, such as machine learning techniques, big data tools, or data preprocessing.
DISCLAIMER
The IGNOU solved assignments and guess papers provided on this platform are for reference purposes only and should not be used to engage in educational dishonesty. These materials serve as learning and study tools and are not intended for submission as original work. Users are responsible for using these materials ethically and in accordance with their educational institution's guidelines. We do not assume liability for any misuse or consequences resulting from the use of these materials. By accessing and utilizing these resources, users agree to this disclaimer.
Chat with us