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Hi All, Our LMS portal is now live, you can register yourself for upcoming internships and trainings, if needed. https://lnkd.in/gpEiJNb4 Thanks

Pandas in Python - The pandas library is a powerful Python package widely used for data manipulation and analysis. It provides data structures and functions to efficiently manipulate large datasets. Some of the key features of pandas include: DataFrame: The primary data structure in pandas is the DataFrame, which is a two-dimensional labeled data structure with columns of potentially different types. It's similar to a spreadsheet or SQL table. Series: A one-dimensional labeled array capable of holding any data type. Data Manipulation: Pandas provides a wide range of functionalities for manipulating data, including merging, reshaping, slicing, indexing, grouping, and filtering datasets. Data Cleaning: It offers methods for handling missing data, removing duplicates, and converting data types. Data Input/Output: Pandas supports various file formats for importing and exporting data, including CSV, Excel, SQL databases, JSON, and more. Time Series Analysis: It has built-in support for working with time series data, including date range generation, frequency conversion, and moving window statistics. Statistical Analysis: Pandas provides descriptive statistics and supports statistical functions for data analysis. Visualization: While pandas itself doesn't offer visualization capabilities, it integrates well with other libraries like Matplotlib and Seaborn for data visualization. Q - Challenge for today - What will be the output of the following Python code? >>>str1="helloworld" >>>str1[::-1] a) dlrowolleh b) hello c) world d) helloworld

RoadMap to Data Science Job : The field of data science is broad and encompasses a range of skills from statistics and mathematics to programming and domain-specific knowledge. Here's a general roadmap for someone aspiring to pursue a career in data science: 1. Learn the Basics: Programming Languages - Python and/or R are commonly used for data science. Start with one and become proficient. Familiarise yourself with libraries such as NumPy, pandas, and scikit-learn (for Python) or the corresponding libraries in R. Mathematics and Statistics - Solid understanding of linear algebra, calculus, and probability/statistics is crucial. 2. Understand Data: Data Cleaning and Preprocessing - Learn how to handle missing data, outliers, and transform data into a usable format. Data Exploration - Visualization tools (matplotlib, seaborn, ggplot in R) to understand the patterns and distributions in the data. 3. Develop Machine Learning Skills: Supervised Learning - Understand algorithms like linear regression, logistic regression, decision trees, and ensemble methods. Unsupervised Learning - Clustering (k-means, hierarchical), dimensionality reduction (PCA). Deep Learning - Familiarize yourself with neural networks, frameworks like TensorFlow or PyTorch. Model Evaluation - Learn metrics for evaluating model performance (accuracy, precision, recall, F1-score, ROC-AUC). 4. Practical Experience: Kaggle - Participate in Kaggle competitions to apply your skills in real-world scenarios and learn from others. Projects - Work on personal or open-source projects to build a portfolio. 5. Big Data Technologies: Familiarize yourself with big data tools like Apache Hadoop and Apache Spark. 6. Database Knowledge: Learn SQL for managing and querying databases. 7. Version Control: Understand version control systems like Git. 8. Domain Knowledge: Depending on your interests, gain knowledge in a specific domain (e.g., finance, healthcare, marketing). 9. Communication Skills: Develop the ability to communicate findings effectively to both technical and non-technical stakeholders. 10. Stay Updated: The field is evolving, so stay updated with new algorithms, tools, and best practices. 11. Networking: Attend meetups, conferences, and connect with professionals in the field. 12. Advanced Topics: Dive deeper into advanced topics like natural language processing, reinforcement learning, etc. 13. Education (Optional): Consider advanced degrees or certifications in data science, machine learning, or related fields. Remember, this roadmap is just a general guide. Tailor it to your interests and career goals. Continuous learning and staying curious are key to success in the dynamic field of data science. Challenge for today - What will be the output of the following Python code? d = {0: 'a', 1: 'b', 2: 'c'} for x, y in d:   print(x, y) a) 0 1 2 b) a b c c) 0 a 1 b 2 c d) Error

SQL Joins - In SQL (Structured Query Language), joins are used to combine rows from two or more tables based on a related column between them. The purpose of joins is to retrieve data that is spread across multiple tables in a relational database. There are several types of joins, each serving a different purpose. Here are some common types: Challenge for today - What will be the output of the following SAS code? data square_dataset;   do i = 1 to 5;    square = i * i;    output;   end; run;   Explanation - output_dataset: This is the name of the dataset that will be created or updated with the results of the loop. index_variable: This is a temporary variable that takes on values from start_value to end_value in increments specified by the by clause. You can use this variable in your loop statements. start_value: The initial value for the index_variable. end_value: The final value for the index_variable. increment: The amount by which the index_variable is incremented in each iteration of the loop.

Resume - Crafting a standout resume involves several key steps: 1. Choose the Right Format: Reverse-Chronological or Functional:  2. Contact Information: 3. Resume Summary/Objective: 4. Work Experience: 5. Education: 6. Skills: 7. Tailor for Each Application: 8. Design and Layout: 9. Proofread: 10. Additional Sections (Optional): Always remember, your resume is your marketing tool and a mirror of yourself. Focus on showcasing your strengths, achievements, and how your skills align with the needs of the prospective employer. Also, tailoring your resume for each job application can significantly increase your chances of getting noticed by recruiters and hiring managers. Challenge for today - What will be the output of the following Python code? x = "abcdef" i = "a" while i in x:    x = x[:-1]    print(i, end = " ")   a) i i i i i i b) a a a a a a c) a a a a a d) none of the mentioned

Credit Cards - Models Credit card data models are essential for managing and processing information related to credit card transactions, customers, accounts, and more. Here's an overview of the typical components of a credit card data model: Cardholder Information: Customer Details: Name, address, contact information, identification details. Account Information: Account number, card number, card type (Visa, Mastercard, etc.), expiration date, card status (active, inactive, canceled). Transaction Data: Transaction Details: Date, time, amount, currency, merchant details (name, location), transaction status (approved, declined). Authorization Information: Approval codes, authorization dates, transaction identifiers. Financial Information: Billing Information: Billing address, billing cycle, statement date. Payment History: Record of payments made, due amounts, minimum payments. Security and Authentication: CVV/CVC: Card Verification Value/Code for security. Tokenization Details: Tokens used for secure transactions. Fraud Detection Information: Patterns, flags, or indicators of potential fraudulent activity. Relationships and Associations: Customer-Account Relationship: Linking multiple cards to a single customer or account. Card Issuer Information: Bank or financial institution details issuing the card. Risk and Compliance: Credit Limits: Maximum amount a cardholder can spend. Credit Scores and Histories: Data related to a cardholder’s creditworthiness. Regulatory Compliance Data: Compliance with financial regulations and standards. Additional Features: Rewards and Loyalty Programs: Data related to points, rewards, or cashback. Notifications and Preferences: Customer preferences for alerts, notifications, or communication. Challenge for today - What will be the output of the following Python code? x = "abcdef" i = "i" while i in x:    print(i, end=" ") a) no output b) i i i i i i … c) a b c d e f d) abcdef 

Credit Cards - Banking Majorly Credit card firms/banks make the most of their money from three things:  1. Interest,  2. Annual fees charged to cardholders and,  3. Transaction fees paid by merchant businesses that accept credit cards. But despite the mushrooming of fintech startups and mobile wallets, many people still wonder — do card companies make money if I pay in full? Why do some credit cards have an annual fee and some are free? Challenge for today - What will be the output of the following Python code? i = 0 while i < 5:    print(i)    i += 1    if i == 3:        break else:    print(0) a) 0 1 2 0 b) 0 1 2 c) error d) none of the mentioned

Key Data Analysis Steps The very first step in Data Analysis is to identify process involved to play with the data and get the desired results : - The first step is to determine the data requirements or how the data is grouped. Data may be separated by age, demographic, income, or gender. Data values may be numerical or divided by category. - The second step in data analytics is the process of collecting it. This can be done through a variety of sources such as computers, online sources, cameras, environmental sources, or through personnel. - The data must be organised after it's collected so it can be analysed. This may take place on a spreadsheet or other form of software that can take statistical data. - The data is then cleaned up before analysis. It's scrubbed and checked to ensure that there's no duplication or error and that it is not incomplete. This step helps correct any errors before it goes on to a data analyst to be analysed. Challenge for today - What will be the output of the following Python code? i = 5 while True:    if i%0O11 == 0:        break    print(i)    i += 1 a) 5 6 7 8 9 10 b) 5 6 7 8 c) 5 6 d) error

What is Data Analytics? Data analytics refers to the process of examining and interpreting raw data to extract meaningful insights, patterns, and trends. It involves using various techniques and tools to analyse data in order to make informed decisions, support business strategies, and discover actionable information. Data analytics is utilised across various fields, including business, science, healthcare, finance, marketing, and more, to turn data into valuable knowledge. Different types of data analytics and its stages are : Descriptive Analytics:  Diagnostic Analytics:  Predictive Analytics:  Prescriptive Analytics: Several Stages of Data analytics Data Collection: - > Data Cleaning and Preparation: - > Exploratory Data Analysis (EDA): - > Data Modelling: - > Interpretation and Insights: - > Visualisation and Reporting: Challenge for today - What will be the output of the following Python code? x = ['ab', 'cd'] for i in x:    i.upper() print(x)   a) [‘ab’, ‘cd’] b) [‘AB’, ‘CD’] c) [None, None] d) none of the mentioned