Top 20 Established Datasets for Sentiment Analysis in 2025

Sentiment Analysis is an opinion-mining technique used to understand human emotions through text, leveraging social media and other user-specific platforms. This technology uses sentiment analysis datasets to provide unique insights into human sentiment by capturing countless moments of expression. When analyzed by machine learning and deep learning models, these datasets reveal patterns that enable businesses and researchers to make better decisions.

Companies use sentiment analysis to remain competitive in the market, gauge customer emotions for their online reputation, and grow their customer base. Social media teams use it to spot trends and respond to customer concerns. Marketing teams measure campaign success and healthcare workers identify unhappy customers who need immediate help.

From social media's raw emotional data to industry-specific insights, each dataset serves a unique purpose in decoding human sentiment. This guide explores 20 established datasets for sentiment analysis in 2025. Let us examine how these resources help businesses bridge the gap between data and human understanding.

1. Social Media Sentiment Datasets

Social media generates massive amounts of data, with people sharing opinions about products, politics, and personal experiences every day. This data from microblogging sites like Twitter, Reddit, and TikTok helps researchers and companies understand public feelings and reactions. They analyze social media sentiments to enhance user engagement and understand the audience’s response to their content. Social media analysis datasets capture real human emotions in natural language and provide training data for sentiment analysis models. As social media platforms provide direct feedback, it helps companies analyze their online presence and stay connected with their customers' needs. Here are the top social media sentiment datasets in detail:

Twitter Political Sentiment Corpus

The Twitter Political Sentiment Corpus dataset contains millions of tweets about political discussions. It uses the Twitter API to collect a corpus of texts that users share as posts on the platform. Each tweet has labels indicating whether it expresses positive, negative, or neutral sentiments. The labels also identify specific emotions like anger, hope, or disappointment.

This Twitter dataset for sentiment analysis uses labeled data to track sentiment changes during major political events. It has the following advantages: 

• Researchers use tweets to understand how public opinion shifts during campaigns. The tweets cover topics such as candidate speeches, debates, and policy announcements.
• Election campaign teams use this data to measure voter reactions. They can identify trending concerns in different regions. 
• The dataset helps predict which messages resonate with voters. Campaign strategists adjust their communication based on these insights.
• The corpus includes metadata such as timestamps, locations, and engagement metrics. This context helps researchers connect sentiment patterns to specific events. 

The dataset is updated regularly to capture new political discussions. You can also build your own Twitter sentiment analysis model with our guide on how to build a Twitter sentiment analysis Python program, which provides a step-by-step tutorial for beginners.

Reddit Mental Health Discourse Dataset

The Reddit Mental Health Discourse Dataset collects discussions from mental health support communities and threads on Reddit’s subreddits. It contains posts and comments where people share experiences with anxiety, depression, and other conditions. Mental health professionals and researchers labeled each text with detailed emotional markers.

The dataset captures complex emotional states that simple positive/negative labels miss. For example, it identifies mixed feelings like "hopeful but anxious" or "sad but grateful." These labels help train AI and machine learning models to understand the complexity of mental health discussions. The dataset targets the following sentiments and uses text classification to map them as follows:

• 0 = Stress
• 1 = Depression
• 2 = Bipolar disorder
• 3 = Personality disorder
• 4 = Anxiety

The data annotations track emotional changes within conversations, showing how community support affects someone's expressed feelings. The Reddit mental health discourse dataset helps in the following ways:

• Researchers use this data to study effective support strategies, and mental health platforms apply these insights to improve their services.
• Healthcare providers use these datasets to make their services more accessible.
• Developers and mental health companies use it to build more empathetic AI support systems.

This sentiment analysis dataset maintains user privacy through careful anonymization. It includes contextual elements such as the time of day and response patterns, helping researchers understand when and how people seek support. These Reddit mental health datasets are available on Kaggle. The corpus grows as new discussions are added to and labeled in subreddits. This ongoing collection captures evolving mental health language and concerns. 

TikTok Comment Emotion Lexicon

The TikTok Comment Emotion Lexicon maps out how users react to viral content. It contains comments from popular videos across different categories and analyzes Gen Z terms and internet slang to label them for text classification. Each comment comes with sentiment labels and emoji interpretations, connecting written emotions to emoji usage patterns.

Users express feelings differently on TikTok than on other platforms. They combine text with emojis to create new emotional expressions. The dataset helps decode these unique communication styles by showing how younger users develop their emotional language. The advantages of TikTok comment emotion lexicon are: 

• Marketing teams use this data to understand Gen Z reactions, tracking how audiences respond to different content types. 
• This sentiment analysis dataset reveals which video styles spark positive engagement, allowing creators to use these insights for content strategy planning.
• The lexicon includes comment threads to show the flow of emotions in conversations, capturing how users build on each other's reactions. 
• This emotion detection data highlights patterns in group emotional responses. 
• Platform moderators use these patterns to identify harmful content early and flag or report it.

YouTube Comment Sentiment Dataset

This sentiment analysis dataset contains millions of comments from YouTube videos. It focuses on comments about products, brands, and content creators and includes annotated comments that reflect audience sentiments. These datasets highlight the power of social media in understanding public sentiment such as:

• Brands use YouTube data to measure public reaction to their videos 
• Content creators analyze what drives positive audience engagement.
• Video platforms use data patterns to rank comments more effectively and promote discussions that encourage constructive engagement.

Important features of the YouTube comment sentiment dataset are:

• The dataset tracks how opinions spread within YouTube communities, showing comment patterns on viral videos and controversial content. 
• It includes comment timestamps to help connect reactions to specific video moments. 
• The dataset reveals which content sections spark the strongest responses, allowing creators to improve their video structure. They learn when to place key messages for maximum impact.
• The dataset includes reply chains, which show how opinions evolve in discussions and track how early comments influence later reactions.

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2. Kaggle’s Top Contenders for 2025 Datasets

Kaggle hosts data science competitions and datasets for machine learning projects. The platform brings together data scientists who share and refine datasets. In 2025, several sentiment analysis datasets stand out for their size and quality. These collections help companies understand customer feelings and opinions. Let’s take a detailed look at the top Kaggle sentiment analysis datasets in 2025:

IMDB Deep Context Reviews

IMDB is a popular platform where movie fans share their thoughts and reviews of films. The IMDB Deep Context Reviews dataset captures movie reviews from its vast user base. Each review reflects viewers' opinions about movies, actors, and directors.

Movie studios need to understand audience reactions to their films. This sentiment analysis dataset on Kaggle helps them track responses to different movie elements. For example, they can see if people enjoy action scenes but dislike the storyline. Studios use these insights to improve their movies. The dataset connects reviews to movie details such as:

• Genre: Horror, Comedy, Thriller, Crime, and more
• Release date
• Box office numbers and overall revenue

This context helps companies analyze viewer opinions and preferences. They can identify patterns, such as horror fans being harder to please than comedy fans.

Review timestamps show how opinions change after a film’s release. When the initial hype fades, early reviews often differ from later ones. Marketing teams use these trends to adjust their promotional strategies, learning when to highlight different movie features.

Multilingual Amazon Product Reviews

Amazon is a global e-commerce platform that sells a wide range of products to consumers worldwide. Its review dataset contains customer opinions in over 15 languages, covering products from electronics to books. These reviews reveal what customers like and dislike about their purchases.

Companies rely on this data to sell products in different countries. Customer preferences vary across cultures and regions. For example, Japanese customers may prioritize different features than Brazilian customers. Sellers use these insights to adapt their products for each market. The multilingual dataset includes product details such as:

• Price and discounts
• Category: Beauty, Wellness, Food, Apparel, and more
• Seller location

This information helps companies understand how these factors influence customer satisfaction. They can determine which price points work best in different regions.

Customer review patterns also show how language affects product perception. Direct translations of product descriptions may miss cultural nuances. Companies use this knowledge to refine their international marketing strategies.

The dataset tracks review changes during sales events like Black Friday, highlighting how discounts impact customer satisfaction. Sellers learn when price cuts enhance or harm product reputation, helping them develop better sales strategies. Verified purchase labels add credibility to the sentiment analysis, allowing companies to prioritize feedback from real buyers and generate more reliable insights for product development.

COVID-19 News Sentiment Timeline

News coverage shaped people's feelings about the COVID-19 pandemic. This dataset tracks how news headlines discuss COVID-19, using headlines from global news sources. Each headline comes with sentiment labels that reflect public emotions during different phases of the pandemic. The dataset reveals when headlines became more hopeful or fearful. For example, vaccine announcements sparked waves of optimism, whereas news about virus variants led to more concerned reporting.

Health organizations use this data to understand public responses to health messages. They analyze which communication approaches are most effective during health crises. The dataset also shows how different countries reported the same events, revealing cultural differences in crisis communication.

The timeline connects headlines to key pandemic events, illustrating how the tone of reporting shifted with case numbers and policy changes. Public health teams use these patterns to plan future crisis responses. They can anticipate how news coverage might influence public behavior. Various types of machine learning models utilize this data to detect emerging health concerns and track shifts in news sentiment. This early warning system helps health agencies prepare for public reactions.

E-commerce Return Feedback Sentiment

This dataset collects customer feedback about product returns from major online stores and e-commerce platforms. It includes return reports with reasons and customer comments, documenting what went wrong with each purchase. E-commerce return feedback sentiments help in the following ways:

• Companies need to understand why customers return products, and this dataset helps identify common issues. For example, size mismatches account for many clothing returns. 
• Businesses use this information to refine product descriptions and size charts. Product teams use these insights to prioritize improvements, focusing on solutions that reduce return rates.
• It also helps companies determine where to add more product details or photos, enabling customers to make more informed purchasing decisions. This sentiment analysis dataset links return reasons to product categories and prices, highlighting which items require better quality control.

Sentiment labels capture customer emotions during the return process. For example, seamless return experiences often lead to more positive feedback. Companies use these insights to enhance their return policies and improve customer satisfaction.

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3. Multilingual & Cross-Cultural Datasets

Analyzing emotions across languages and cultures helps global businesses develop successful strategies and build better solutions. Companies need datasets that capture how different cultures express emotions. These datasets help create AI systems that accurately interpret customer sentiment worldwide, revealing how cultural backgrounds influence customer reactions. Here are the top multilingual and cross-cultural datasets for sentiment analysis:

Global Customer Support Transcripts

This dataset contains customer service conversations in approximately 25 languages. Global Customer Support Transcripts include phone calls, chat logs, and email exchanges from multinational companies. Each interaction demonstrates how customers express concerns and receive assistance.

The conversations reveal cultural differences in how customers express frustration. For example, American customers tend to state problems directly, while Japanese customers often express concerns more indirectly. Customer support teams use these insights to tailor training for different regions.

This sentiment analysis dataset tracks emotional shifts during problem resolution, showing when a customer's mood transitions from frustration to satisfaction. It has the following applications:

• Companies analyze these turning points to refine service scripts and identify which responses work best in different cultures.
• Support managers use this data to assess agent performance, evaluating how different approaches impact customer sentiment. 
• The dataset helps develop more effective training programs for support teams, identifying communication styles that foster customer trust.

Patterns in spoken language also reveal implicit customer needs. In one culture, a pause may signal agreement, while in another, it could indicate hesitation or disagreement. AI systems trained on this dataset learn to detect and interpret these subtle cues, leading to more responsive and culturally aware customer support.

UNESCO Cultural Heritage Sentiment

This dataset tracks public opinions about World Heritage sites through social media and visitor reviews. It contains comments on more than 1,000 cultural locations worldwide, with each review reflecting how people value different aspects of cultural heritage. 

Tourism boards use this dataset to enhance site preservation by identifying the features visitors appreciate most. The applications of this dataset are:

• It also reveals how local communities perceive tourism’s impact, helping balance tourism growth with cultural protection
• Site managers can analyze this dataset to monitor changing visitor attitudes over time
• It helps identify early signs of issues such as overcrowding
• The dataset highlights which preservation efforts resonate with the public

The UNESCO Cultural Heritage Sentiment Analysis Dataset helps predict future heritage tourism trends. It identifies sites attracting increasing interest and assists UNESCO in allocating resources for site protection.

Emoji-Enhanced Multilingual Tweets

This dataset transforms our understanding of emotions across language barriers. It contains tweets in over 30 languages, each emoji linked to specific emotional meanings. Twitter users worldwide express feelings through unique emoji combinations.

The dataset maps how different cultures use emojis to convey emotions. For example, the "crying" emoji represents laughter in some Asian countries but sadness in Western nations. Companies use these cultural distinctions to avoid misinterpreting customer sentiment.

The collection uncovers new patterns in emotional expression. Users often combine emojis to create nuanced feelings that words alone cannot capture. For instance, an "angry face emoji" followed by a "fist emoji" might symbolize determination in one culture but anger in another. Social media teams leverage these insights to craft culturally appropriate responses.

The dataset also tracks the evolution of emoji usage. As users develop new ways to express emotions, emerging emoji combinations gain popularity. Marketing teams analyze these trends to ensure their messaging remains relevant and culturally attuned.

Multilingual News Headlines Sentiment

The Multilingual News Headlines Sentiment Dataset examines how global news sources report the same events. It includes headlines in more than 20 languages, showing how different cultures interpret global events.

The dataset reveals cultural biases in news reporting. It highlights how political events may receive positive coverage in one country but negative coverage in another. Media analysts use these insights to understand global perspectives on major issues.

The dataset connects headlines to local cultural events and values, illustrating how national priorities shape news sentiment. For example, environmental news tends to feature stronger emotional language in countries recently affected by climate disasters.

A breaking story often begins with neutral language and gradually adopts an emotional tone as it spreads. News organizations use this dataset to track how stories evolve across borders. Machine learning models apply this data to:

• Detect news bias
• Compare how different media outlets cover the same events

These insights help readers understand multiple perspectives on global issues.

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4. Industry-Specific Sentiment Resources

Companies rely on specialized datasets to analyze customer sentiments and opinions in their field. Each industry encounters unique concerns and technical language. Benchmark datasets, such as healthcare feedback, fintech call sentiments, and the gaming community toxicity index, help businesses interpret emotions within their market context. These datasets compile reviews, conversations, and public comments about industry services.

Let us study these industry-specific sentiment analysis datasets in detail: 

Healthcare Patient Feedback Corpus

This dataset gathers domain-specific corpora (healthcare-specific textual data) of patient feedback and experiences from healthcare review websites and hospital feedback forms. It includes patient comments about doctors, hospitals, and medical treatments. Patients share stories about their care journey, discussing factors such as:

• Waiting time 
• Doctor communication
• Treatment results
• Overall experience

The dataset highlights key emotional moments in a patient's healthcare journey. For instance, it detects when patients feel anxious before surgery or relieved after recovery.

Hospitals use this feedback to improve patient care by identifying which aspects of treatment cause stress and which provide reassurance. The dataset links patient sentiments to specific hospital departments and procedures, helping medical teams focus their improvement efforts.

It also uncovers communication gaps between doctors and patients. Medical jargon can confuse or worry patients, and hospitals use these insights to train doctors to communicate clearly. This allows healthcare professionals to explain treatments in ways that ease patient anxiety.

Financial Earnings Call Sentiment

The financial earnings call sentiment dataset analyzes earnings call transcripts from public companies to study how company leaders discuss business performance. Each speech is labeled with confidence, worry, or uncertainty.

Market analysts track these emotional signals to predict stock movements. They notice when CEOs sound less sure about plans. The dataset connects speech patterns to later company performance, helping investors make more informed decisions. The collection shows how different industries discuss financial challenges, such as:

• Tech CEOs often express optimism during product delays.
• Bank leaders tend to use more cautious language regarding future growth.

Investors use these patterns to understand company messaging better.

The dataset tracks changes in leader confidence over yearly quarters. It highlights when management's tone shifts from positive to worried. Trading algorithms use these clues to identify early warning signs of company health. Speech patterns also reveal unspoken company issues. Leaders might use vague language when facing difficulties. Market watchers rely on these subtle signals to assess company stability.

Gaming Community Toxicity Index

The gaming community toxicity index examines player interactions in major online gaming communities. It contains chat messages from popular multiplayer games, each showing how players communicate with teammates and opponents during gameplay. Companies use this data to foster healthier online spaces. They track when friendly banter escalates into harassment. The dataset flags different types of toxic behavior, ranging from mild trash talk to serious threats, helping moderators intervene at the right time.

The collection reveals how game events trigger toxic responses. For example, players often become more hostile after losing streaks or technical problems. Game designers use these patterns to introduce features that diffuse heated moments. For example, they might add longer breaks between matches.

The dataset connects player behavior to game mechanics. Some game types create more tension than others, and team games often foster both strong friendships and intense conflicts. Developers use these insights to design games that encourage teamwork.

Podcast Transcript Sentiment Labels

This dataset analyzes emotions in podcast episodes and includes shows about news, entertainment, and education. Each transcript comes with markers for speaker tone and emotional shifts. Podcast networks use this data to:

• Understand what engages listeners
• Track when hosts create emotional connections with their audience
• Identify which discussion topics spark strong listener responses

This helps producers plan more engaging content. The collection reveals how different podcast styles affect listeners' emotions. Interview shows often create a deeper emotional impact than solo presentations. News podcasts experience more emotional variation than technical shows. Creators use these patterns to structure their episodes more effectively.

The dataset's timestamps track emotional flow throughout episodes. For example, strong openings often lead to better listener retention. The dataset also identifies ideal moments for serious topics or lighter segments, which producers use to improve episode pacing.

Speaker patterns show how conversation styles influence message impact. Some hosts connect better through personal stories, while others engage more through questions and debate. Networks use these insights to match hosts with show formats. The dataset also tracks how sound effects and music enhance emotional moments. Background elements can strengthen or weaken the speaker's emotional message.

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5. Emerging 2025 Dataset Trends

The sentiment analysis field continues to grow with new data types and sources. Researchers now use AI to fill gaps in emotional data collection. Machine learning techniques are transforming how we understand text-based sentiments. The latest trends focus on recognizing subtle emotions and global issues. Let’s discuss the latest sentiment analysis dataset:

Synthetic Data for Rare Sentiments

Datasets combine real and AI-generated text samples to capture hard-to-find emotional expressions. Traditional datasets often miss complex emotions like sarcasm or mixed feelings. AI helps generate more examples of these rare cases.

AI-generated datasets address challenges in sentiment research through:

1. Sarcasm Detection: Traditional methods struggle with complex emotional tones. To address this:

• AI creates synthetic examples mimicking intricate language patterns
• Generates realistic, sarcastic text scenarios

2. Niche Emotional Mapping: 

• Captures rare emotional states missed by conventional datasets
• Produces training data for uncommon sentiment expressions
• Helps models recognize subtle emotional nuances

The dataset demonstrates how context changes emotional meaning. A simple "great" might have opposite meanings in different situations. The synthetic data examples help AI systems learn these contextual clues, improving chatbots and customer service systems in detecting real customer emotions.

The synthetic data matches writing patterns from different age groups and cultures. It creates examples of how teens express irony differently from adults. Social media companies use this data to analyze user emotions more accurately.

Each synthetic example includes notes about its emotional elements, helping researchers study how different feelings combine in human expression.

Climate Change Opinion Atlas

The dataset tracks how people worldwide talk about climate change online and in surveys. People express different levels of worry about climate change. They show eco-anxiety through daily social media posts about weather changes and hope when sharing news about green technology. Policymakers use these emotional patterns to shape climate messages. Social media and survey data track global sentiment on environmental issues through sentiment tracking and data collection models:

• Sentiment tracking performs the following function:
  • Monitors public reactions to climate policies
  • Captures emotional responses across different regions
  • Tracks shifts in eco-anxiety levels
• Data Collection Methods and data mining techniques are performed through:
  • Social media sentiment analysis
  • Global survey responses
  • Academic research platforms
  • Environmental policy forums

The collection tracks how climate discussions change over time. It shows when the public’s focus shifts between problems and solutions. Climate scientists use this to make their research more relevant to public concerns. This pattern in public opinion reveals which climate solutions garner more public support. Policymakers use this knowledge to build better climate action plans.

Privacy-Compliant Voice Assistant Logs

Privacy-compliant voice assistant logs capture emotional patterns from voice commands while protecting user privacy. It contains anonymized voice interactions from smart speakers and phone assistants to maintain the principles of AI ethics. Engineers remove personal details but keep the emotional markers in each voice sample.

It shows how people express feelings through voice commands. Frustration often occurs in repeated requests or volume changes, while satisfaction shows in voice tone after successful task completion. AI developers use these patterns to create more responsive voice assistants. It has the following features:

• It connects voice emotions to the time of day and request types. For example, morning commands often sound more rushed than evening ones. Different tasks elicit different emotional responses, and device makers use this knowledge to adjust how their assistants respond.
• Focuses on natural conversation flow. It shows when users feel comfortable talking to AI versus when they feel strange. This makes voice interactions feel more normal, and AI assistants learn to match user energy levels.
• Reveals user trust levels with AI. Some people speak formally to assistants, while others talk casually. Developers use these differences to create AI responses that match user comfort levels.

AI-Generated Sarcasm Detection Dataset

This dataset helps computers understand when people mean the opposite of what they say. It includes examples of sarcastic comments generated by AI and verified by humans. Each example shows how context and tone create sarcastic meaning. The AI-generated sarcasm detection dataset breaks sarcasm down into different types:

• It detects cultural patterns in sarcasm use. What sounds sarcastic in one culture might seem sincere in another. Content moderators use these insights to avoid misunderstanding user intentions.
• Time patterns show when people use more sarcasm online. Certain topics or events trigger more sarcastic responses. Platform managers use this information to adjust their content filters.
• Some examples show playful teasing, while others express criticism. AI systems learn to spot these differences through word choice and context clues. Social media companies use this to better moderate comments.

Each example includes notes about its sarcastic elements, which help machines learn the building blocks of sarcastic expression.

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How upGrad Can Help You

upGrad is an upskilling platform that offers practical data science training to professionals who want to master sentiment analysis. The platform combines education with real industry experience. Students work on opinion mining projects and Natural Language Processing (NLP) projects while learning from experts who use these skills daily. Here is how upGrad provides a one-stop solution for your learning:

Industry-Aligned Certification Programs

upGrad's certification programs teach the latest sentiment analysis methods that companies need to enhance their services. Students learn to work with major datasets and use industry-standard tools. Each course includes hands-on projects with real company data. The certifications and course programs focus on the following:

• Building sentiment analysis models from scratch
• Working with enterprise-level datasets
• Using NLP Libraries for Python for text analysis
• Creating sentiment analysis pipelines
• Implementing machine learning algorithms
• Deploying models in production environments

Companies recognize these certifications because students demonstrate their skills through real projects. Each program is designed based on the student’s skill level and specific industry, ensuring students learn the exact skills employers seek.

The table below lists the top upGrad certification courses that you must explore to become a successful data scientist:

Mentorship and Networking Opportunities

Success in sentiment analysis requires more than technical skills. upGrad connects learners with industry leaders and data scientists who work at major tech companies. These mentors share practical knowledge to help students learn how companies use sentiment analysis datasets and tools to solve business problems.

The mentor network at upGrad includes professionals from companies like Amazon, Google, and Microsoft. They guide students through sentiment analysis projects and topics, offering career advice. Students join a community of data professionals who help each other grow. Our mentorship assistance includes:

• Direct guidance from senior data scientists
• Interaction with industry experts
• Alumni network spanning global tech companies
• Insider insights into career development

Career Transition Support

upGrad helps students turn their sentiment analysis and data analytics skills into career opportunities. The career support team works with each student to:

• Create portfolios showcasing sentiment analysis projects
• Build resumes that highlight technical skills
• Practice NLP and Machine Learning interview questions
• Connect with companies hiring data scientists

The platform partners with companies that need sentiment analysis and data science experts. These partnerships lead to internships and full-time positions. Students get direct access to hiring managers at partner companies. They also receive:

• Mock interviews with industry experts
• Feedback on project presentations
• Tips for technical assessments
• Guidance on company selection
• Support during job transitions

The Bottom Line

In 2025, quality data will drive data research through these top 20 sentiment analysis datasets, which have become harbingers of emotional intelligence in technology. Companies that choose the right datasets gain deeper insights into customer needs. They build AI systems that respond to emotions more accurately, creating better customer experiences and stronger business relationships.

The future brings more specialized datasets for specific industries and emotions. AI-generated data helps fill gaps in our understanding of complex feelings. The collection methods focus on privacy to protect user rights while gathering emotional insights. These advances make sentiment analysis more powerful and responsible.

Are you unsure which career path best suits you? Talk to upGrad’s experts and counselors for one-on-one guidance on various careers and courses. 

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1. Basic Python Programming Free Certification Course
2. Data Structures and Algorithms Free Certification Course
3. Logistic Regression for Beginners Free Certification Course
4. Introduction to Database Design with MySQL Free Certification Course

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