Data Science Course (1)

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• Exploratory Data Analysis (EDA) Unveiling
  Insights
• Data Profiling and Summary Statistics
• Start by conducting data profiling to understand
  the structure and characteristics of the
  dataset. Data Science Course. Compute summary
  statistics such as mean, median, standard
  deviation, and quartiles for numerical variables,
  and frequency distributions for categorical
  variables. This initial exploration provides
  insights into the data's distribution, central
  tendencies, and variability.
• Visualization Techniques
• Utilize a variety of visualization techniques to
  explore the relationships and patterns within
  the dataset. Create histograms, box plots, and
  density plots to visualize the distribution of
  numerical variables. Use bar charts, pie charts,
  and heat maps to analyze categorical variables
  and their relationships. Scatter plots and pair
  plots can reveal correlations and associations
  between variables.
• Handling Missing Values and Outliers
• Identify and handle missing values and outliers
  in the dataset. Visualize missing data patterns
  using heatmaps or bar plots to understand the
  extent of missingness across variables. Employ
  techniques such as imputation, deletion, or
  advanced methods like predictive modeling to
  handle missing values. Use box plots, scatter
  plots, or z-score analysis to detect and address
  outliers appropriately.
• Feature Engineering and Transformation
• Explore feature engineering techniques to derive
  new variables or transform existing ones. Create
  new features by combining or aggregating existing
  variables to capture meaningful patterns in the
  data. Perform transformations such as logarithmic
  or polynomial transformations to normalize
  skewed distributions and improve model
  performance.
• Correlation Analysis and Dimensionality
  Reduction

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(PCA) or t-distributed stochastic neighbor
embedding (t-SNE) to visualize high-dimensional
data and uncover underlying structures. By
incorporating these pointers into exploratory
data analysis, analysts can gain valuable
insights into the dataset's characteristics,
relationships, and patterns. Data Science Course
in Mumbai. EDA serves as a crucial step in the
data analysis process, enabling analysts to make
informed decisions, identify potential issues,
and formulate hypotheses for further
investigation. 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
9108238354, Email enquiry_at_excelr.com