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Core Skills Covered in a Data Science Course
1. Programming Proficiency in Python and R
A strong foundation in programming is essential for data scientists. Data Science Course.
Courses typically focus on Python and/or R—two of the most widely used languages in the data
science ecosystem. Learners are trained in writing efficient code for data manipulation, analysis,
and automation. They also explore essential libraries such as NumPy, Pandas, Matplotlib, and
Scikit-Learn for Python, or ggplot2 and dplyr in R.
2. Statistical and Mathematical Foundations
Understanding the principles of statistics, linear algebra, and calculus is crucial for making
sense of data and building effective models. Data science courses cover topics like probability
distributions, hypothesis testing, statistical inference, and linear regression. These skills help
learners interpret data accurately and support data-driven decision-making.
3. Data Wrangling and Cleaning Techniques
Real-world data is often messy. Courses emphasize practical techniques for collecting, cleaning,
transforming, and structuring raw data for analysis. Students learn how to deal with missing
values, detect outliers, convert data formats, and normalize or scale data—essential tasks for
preparing datasets for machine learning or business intelligence tasks.
4. Machine Learning and Predictive Modeling
Data science training includes building, evaluating, and optimizing machine learning models.
Topics span supervised learning (e.g., classification, regression), unsupervised learning (e.g.,
clustering, dimensionality reduction), and sometimes even deep learning. Learners are taught
how to choose the right algorithms, tune parameters, and assess performance using
cross-validation and error metrics.
5. Data Visualization and Communication
Being able to communicate findings effectively is just as important as technical know-how.
Courses teach how to use tools like Matplotlib, Seaborn, Plotly, or Tableau to create compelling
visuals. Data Science Course in Mumbai. Students learn storytelling with data—turning
analytical results into actionable insights through dashboards, reports, and presentations
tailored for non-technical stakeholders.
Business name: ExcelR- Data Science, Data Analytics, Business Analytics Course Training
Mumbai
Address: 304, 3rd Floor, Pratibha Building. Three Petrol pump, Lal Bahadur Shastri Rd,
opposite Manas Tower, Pakhdi, Thane West, Thane, Maharashtra 400602
Phone: 09108238354,
Email: enquiry@excelr.com

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Core Skills Covered in a Data Science Course.pdf

  • 1. Core Skills Covered in a Data Science Course 1. Programming Proficiency in Python and R A strong foundation in programming is essential for data scientists. Data Science Course. Courses typically focus on Python and/or R—two of the most widely used languages in the data science ecosystem. Learners are trained in writing efficient code for data manipulation, analysis, and automation. They also explore essential libraries such as NumPy, Pandas, Matplotlib, and Scikit-Learn for Python, or ggplot2 and dplyr in R. 2. Statistical and Mathematical Foundations Understanding the principles of statistics, linear algebra, and calculus is crucial for making sense of data and building effective models. Data science courses cover topics like probability distributions, hypothesis testing, statistical inference, and linear regression. These skills help learners interpret data accurately and support data-driven decision-making. 3. Data Wrangling and Cleaning Techniques Real-world data is often messy. Courses emphasize practical techniques for collecting, cleaning, transforming, and structuring raw data for analysis. Students learn how to deal with missing values, detect outliers, convert data formats, and normalize or scale data—essential tasks for preparing datasets for machine learning or business intelligence tasks. 4. Machine Learning and Predictive Modeling Data science training includes building, evaluating, and optimizing machine learning models. Topics span supervised learning (e.g., classification, regression), unsupervised learning (e.g.,
  • 2. clustering, dimensionality reduction), and sometimes even deep learning. Learners are taught how to choose the right algorithms, tune parameters, and assess performance using cross-validation and error metrics. 5. Data Visualization and Communication Being able to communicate findings effectively is just as important as technical know-how. Courses teach how to use tools like Matplotlib, Seaborn, Plotly, or Tableau to create compelling visuals. Data Science Course in Mumbai. Students learn storytelling with data—turning analytical results into actionable insights through dashboards, reports, and presentations tailored for non-technical stakeholders. Business name: ExcelR- Data Science, Data Analytics, Business Analytics Course Training Mumbai Address: 304, 3rd Floor, Pratibha Building. Three Petrol pump, Lal Bahadur Shastri Rd, opposite Manas Tower, Pakhdi, Thane West, Thane, Maharashtra 400602 Phone: 09108238354, Email: enquiry@excelr.com