Data Science
Data science is the process of extracting insights and knowledge from data using various techniques, such as machine learning, statistical modeling, and data vi...
Data science is the process of extracting insights and knowledge from data using various techniques, such as machine learning, statistical modeling, and data vi...
Data science is a multidisciplinary field that extracts insights and knowledge from structured and unstructured data using various techniques, such as machine learning, statistical modeling, and data visualization.
Data science involves:
1. Data collection: Gathering data from various sources, such as databases, files, and online platforms.
2. Data cleaning: Ensuring the quality and accuracy of the data by handling missing values, outliers, and inconsistencies.
3. Data analysis: Using statistical and machine learning techniques to identify patterns, trends, and correlations within the data.
4. Data visualization: Presenting the insights and findings in a clear and concise manner using visualization tools, such as plots, charts, and heatmaps.
5. Insight generation: Interpreting the results and extracting actionable insights that can inform business decisions or solve complex problems.
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Basic understanding of statistics and probability
Proficiency in using data analysis tools (Jupyter Notebooks, Pandas, NumPy)
Familiarity with programming concepts (preferably Python or R)
Ability to work with structured and unstructured data
Strong analytical and problem-solving skills
Develop a strong foundation in data science concepts and its applications across various industries.
Understand the data science lifecycle, including data collection, processing, analysis, and visualization.
Learn to work with data analytics tools such as Jupyter Notebooks, Pandas, NumPy, and Scikit-learn for data manipulation and analysis.
Master data exploration and visualization techniques using tools like Matplotlib, Seaborn, and Tableau to derive insights from data.
Gain expertise in data cleaning and preprocessing, including handling missing data, outliers, feature engineering, and transformation techniques.
Learn statistical analysis and hypothesis testing, covering inferential statistics, A/B testing, and regression analysis for data-driven decision-making.
Explore the fundamentals of machine learning, including supervised and unsupervised learning, model evaluation, and hyperparameter tuning.
Understand advanced topics such as time series analysis, natural language processing (NLP), and deep learning basics.
Learn how to deploy machine learning models and operationalize them for real-world applications.
Apply acquired knowledge to a final project, solving a real-world data problem and demonstrating end-to-end data science proficiency.
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Students
1
language
English
Duration
00h 00mLevel
intermediate
Expiry period
6 Months
Certificate
Yes
TTA Admin
English
Certificate Course
1 Students
00h 00m
11 Courses
2 Reviews