Python Data Science Projects to Level Up Your Skills#

Overview#

The first step to any successful data science initiative is proper data cleaning and an in-depth exploratory data analysis (EDA). This project’s goal is to develop your foundational skills in reading, cleaning, and summarizing datasets, typically using Pandas. You’ll learn how to handle missing values, detect outliers, rename columns, merge datasets, and create pivot tables. By the end of the project, you’ll have a cleaned dataset ready for further exploration or modeling.

Key Steps#

1. Import data from CSV or Excel files using Pandas.
2. Inspect the dataset with methods like df.head(), df.info(), and df.describe().
3. Fix inconsistencies, such as incorrect column names or data types.
4. Handle missing values with techniques like imputation or row removal.
5. Identify and remove duplicates or outliers using domain knowledge.
6. Summarize insights with basic charts (histograms, box plots, etc.).

Example Code Snippet#

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import pandas as pd
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# Load a CSV file
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df = pd.read_csv("data.csv")
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# Quick overview
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print(df.head())
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print(df.info())
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# Handle missing values by dropping rows with NaN
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df_cleaned = df.dropna()
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# Alternatively, fill missing with mean or median
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df['some_column'].fillna(df['some_column'].mean(), inplace=True)
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# Remove duplicates
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df_cleaned.drop_duplicates(inplace=True)
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# Convert data types if necessary
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df_cleaned['date_col'] = pd.to_datetime(df_cleaned['date_col'])
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# Show descriptive stats
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print(df_cleaned.describe())

Use a public dataset from Kaggle or other open data portals to practice. A typical scenario might involve analyzing housing prices, retail sales, or a collection of product reviews. The focus here is to become comfortable working with Python’s data structures, indexing, slicing, and basic transformation methods commonly applied to real datasets.

Overview#

After cleaning your data, you’ll want to visualize it to uncover trends and patterns. Visualization is key because it delivers insights in a way that is intuitive and easy to communicate. Matplotlib, Seaborn, and Plotly are the popular libraries for data visualization in Python, but for this project, we’ll focus on Matplotlib and Seaborn. You’ll learn to create line plots, bar charts, histograms, scatter plots, and more complex visuals like box plots or pair plots for multivariate analysis.

Key Steps#

1. Install Matplotlib and Seaborn if needed (pip install matplotlib seaborn).
2. Import your cleaned dataset from Project 1.
3. Create univariate plots (histograms, KDE plots) for individual features.
4. Create bivariate plots (scatter plots, correlation heatmaps) to see relationships between features.
5. Customize plots with titles, labels, legends, and color schemes in Seaborn.

Sample Matplotlib and Seaborn Code#

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import matplotlib.pyplot as plt
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import seaborn as sns
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# Assuming df_cleaned is your DataFrame
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plt.figure(figsize=(8,6))
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sns.histplot(df_cleaned['some_numeric_feature'], bins=30, kde=True)
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plt.title("Distribution of Some Numeric Feature")
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plt.xlabel("Feature Value")
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plt.ylabel("Frequency")
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plt.show()
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# Scatter plot to examine relationship between two variables
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plt.figure(figsize=(8,6))
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sns.scatterplot(data=df_cleaned, x='feature_x', y='feature_y')
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plt.title("Feature X vs Feature Y Scatter Plot")
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plt.xlabel("Feature X")
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plt.ylabel("Feature Y")
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plt.show()

Try to build at least five different chart types, focusing on how each type highlights certain aspects of your data. For instance, a line chart might be better for tracking change over time, while a scatter chart can help identify correlations between two numerical features.

Overview#

Once data cleaning and EDA are complete, the next step is often to build a predictive model. Our third project introduces basic machine learning techniques using the scikit-learn library. You can choose a classification or regression problem depending on your dataset. For classification, think of tasks like predicting whether a customer will churn. For regression, you might predict house prices.

Choosing a Model#

Some common models you might start with:

• Logistic Regression (for classification)
• Decision Trees
• Random Forests
• Linear Regression (for regression tasks)
• Gradient Boosted Models (e.g., XGBoost, though that’s an external library)

Below is a small table summarizing some attributes of popular ML models:

Example Code Snippet#

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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import accuracy_score
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# Example for classification:
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data = pd.read_csv("classification_data.csv")
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# Separate features and target
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X = data.drop("target", axis=1)
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y = data["target"]
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# Split into training and testing sets
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X_train, X_test, y_train, y_test = train_test_split(X, y,
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                                                    test_size=0.2,
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                                                    random_state=42)
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# Initialize and train model
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model = RandomForestClassifier(n_estimators=100, random_state=42)
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model.fit(X_train, y_train)
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# Predictions and evaluation
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y_pred = model.predict(X_test)
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print("Accuracy:", accuracy_score(y_test, y_pred))

To make this project more robust, experiment with different hyperparameters, cross-validation, grid search, or random search. Analyze whether the model is overfitting or underfitting using metrics like accuracy, precision, recall, and F1-score for classification tasks, or RMSE and R² for regression tasks.

Overview#

Natural Language Processing (NLP) is a field dedicated to enabling machines to understand and interpret human language. Text data is one of the most common data types you’ll encounter. This project will involve tokenizing text, removing stop words, performing sentiment analysis, and potentially building a text-classification model. You’ll get to explore libraries like NLTK or spaCy, as well as more advanced topics such as word embeddings.

Key Steps#

1. Clean and preprocess text data to remove noise (punctuation, special characters, etc.).
2. Tokenize text into words or subwords.
3. Remove or handle stop words (common words like “the,” “and,” “is”).
4. Convert text to numerical features using Bag-of-Words, TF-IDF, or word embeddings.
5. Build a classifier (e.g., Naive Bayes, Logistic Regression) for sentiment or topic classification.

Example Code Snippet with NLTK#

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import nltk
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from nltk.corpus import stopwords
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from nltk.tokenize import word_tokenize
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from sklearn.feature_extraction.text import TfidfVectorizer
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nltk.download('punkt')
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nltk.download('stopwords')
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sentences = [
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    "Python is a great programming language for data science.",
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    "I love analyzing data with Python!",
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    "Text cleaning is crucial in NLP projects."
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]
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# Tokenize
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tokens = [word_tokenize(sentence.lower()) for sentence in sentences]
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# Remove stopwords
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stop_words = set(stopwords.words('english'))
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cleaned_tokens = []
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for token_list in tokens:
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    filtered = [w for w in token_list if w not in stop_words]
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    cleaned_tokens.append(filtered)
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# Convert sentences to TF-IDF
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vectorizer = TfidfVectorizer()
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X = vectorizer.fit_transform([" ".join(tokens) for tokens in cleaned_tokens])
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print(X.toarray())

Experiment with other tasks such as part-of-speech tagging, named entity recognition, and advanced vector representations like Word2Vec or GloVe to deepen your NLP expertise.

Overview#

When datasets become extremely large, traditional in-memory operations might become inefficient. Apache Spark is a framework optimized for distributed computing. PySpark, the Python interface to Spark, allows you to scale out your data processing tasks to multiple machines without having to drastically rewrite your data science pipelines. By working on a PySpark project, you’ll learn to handle large-scale data, run distributed machine learning, and improve the efficiency of your data science workflows.

Key Steps#

1. Install Apache Spark or run it on a platform like Databricks.
2. Use PySpark DataFrames to load large CSV or Parquet files.
3. Perform distributed transformations (filter, groupBy, join, etc.).
4. Utilize Spark’s machine learning library (MLlib) for modeling on large datasets.
5. Compare runtime and resources used against normal Pandas scripts.

Example Code Snippet with PySpark#

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from pyspark.sql import SparkSession
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# Initialize Spark session
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spark = SparkSession.builder \
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    .appName("BigDataProject") \
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    .getOrCreate()
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# Load data
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df_spark = spark.read.csv("big_data.csv", header=True, inferSchema=True)
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# Show top rows
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df_spark.show(5)
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# Example transformation
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df_filtered = df_spark.filter(df_spark["some_column"] > 100)
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# Aggregation
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df_agg = df_filtered.groupBy("category").count()
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df_agg.show()
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# Convert to Pandas for local analysis (be cautious with large data!)
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df_agg_pandas = df_agg.toPandas()
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print(df_agg_pandas)

Use caution when calling .toPandas() on huge dataframes, as it brings the data back to your local machine’s memory. Ideally, stay within Spark for both data processing and model training to leverage distributed computing’s full benefits.

Overview#

Deep learning is a subfield of machine learning that uses neural networks with multiple layers to learn hierarchical representations from data such as images, text, and more. Projects here might range from image classification using Convolutional Neural Networks (CNNs) to text classification using Recurrent Neural Networks (RNNs) or Transformers. TensorFlow (particularly Keras) and PyTorch are the two most popular deep learning frameworks in Python, each with extensive community support.

Key Steps#

1. Pick a framework: TensorFlow/Keras or PyTorch.
2. Identify your dataset (e.g., MNIST for digit classification if you’re starting out).
3. Build a simple neural network architecture and compile it.
4. Train the model and track performance metrics.
5. Evaluate using test data, and examine confusion matrices or other relevant metrics.

TensorFlow/Keras Example Code#

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import tensorflow as tf
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense, Flatten
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from tensorflow.keras.datasets import mnist
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# Load and preprocess data
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_train = x_train / 255.0
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x_test = x_test / 255.0
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# Define a simple model
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model = Sequential([
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    Flatten(input_shape=(28, 28)),
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    Dense(128, activation='relu'),
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    Dense(10, activation='softmax')
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])
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# Compile and train
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model.compile(optimizer='adam',
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              loss='sparse_categorical_crossentropy',
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              metrics=['accuracy'])
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model.fit(x_train, y_train, epochs=5)
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# Evaluate
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test_loss, test_acc = model.evaluate(x_test, y_test)
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print("Test Accuracy:", test_acc)

For a more advanced experience, experiment with CNN layers for image data or RNN/LSTM layers for time series or text data. Tune hyperparameters like learning rate, batching strategy, and epochs. You can also explore GPU acceleration, which significantly speeds up training.

Overview#

Time series data is everywhere—from stock prices to server logs. Forecasting involves predicting future values based on historical patterns. Python’s statsmodels library provides classical modeling (ARIMA, SARIMA), while Facebook’s Prophet library is particularly user-friendly for time series forecasting tasks. This project will teach you the peculiarities of time-indexed data, where order and stationarity aspects become critical.

Key Steps#

1. Ensure your dataset has a proper datetime index.
2. Explore trends, seasonality, and stationarity of the series.
3. Split the data into training and test sets based on time (avoid random splitting).
4. Use classical ARIMA/SARIMA or Prophet for forecasting.
5. Evaluate forecasts using metrics like MAPE (Mean Absolute Percentage Error).

Example Code Snippet with Prophet#

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from prophet import Prophet
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import pandas as pd
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# DataFrame must have columns ds (date) and y (value)
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df = pd.read_csv("time_series_data.csv")
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df['ds'] = pd.to_datetime(df['ds'])  # rename your date column to ds
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df['y'] = df['your_value_column']
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model = Prophet()
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model.fit(df)
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future = model.make_future_dataframe(periods=30)  # 30 days forecast
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forecast = model.predict(future)
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model.plot(forecast)

Try implementing additional regressors such as seasonal events or holidays if relevant. Compare your Prophet results with an ARIMA model from statsmodels to see differences in performance and interpretability.

Overview#

As your skills grow, you’ll find that building a model isn’t the end of the journey. Productionizing models, also known as MLOps (Machine Learning Operations), involves the deployment, monitoring, and governance of models. You might package your Python application in a Docker container, push it to a container registry, and then run it on a cloud service like AWS, Azure, or Google Cloud. This final project helps you think about the entire lifecycle of a data science solution—from local experimentation to scalable production deployments.

Key Steps#

1. Containerize your data science application using Docker.
2. Use a CI/CD pipeline (e.g., GitHub Actions, GitLab CI) to automate tests and deployment.
3. Deploy to a cloud platform (AWS Elastic Container Service, Google Cloud Run, Azure Container Instances, etc.).
4. Implement monitoring for model drift and data shifts over time.
5. Automate retraining or re-deployment workflows as data changes.

Dockerfile Example#

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# Use a standard Python base image
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FROM python:3.9-slim
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# Create working directory
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WORKDIR /app
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# Copy requirement file and install
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy the rest of the code
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COPY . .
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# Expose port (if needed)
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EXPOSE 8080
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# Define default command
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CMD ["python", "main.py"]

Automating these steps is crucial. For instance, you could have a pipeline that automatically retrains a model every week if certain KPIs are not met. By adopting MLOps best practices, your skill set extends beyond coding scripts into the realm of robust software engineering practices, making you a stronger data scientist in professional settings.

1. Experiment with Different Data Types#

Don’t limit your experience to basic CSV datasets. Look into image processing, audio analysis, or geospatial data. Libraries like OpenCV can handle image manipulations, while specialized pipelines exist for audio signals—pushing you further into specialized or niche areas of data science.

2. Explore GPU and TPU Acceleration#

If you’re delving into deep learning, consider exploring GPU acceleration in the cloud (AWS, Google Cloud, or Azure). Tensor Processing Units (TPUs) offered by Google can massively speed up training times for large neural network architectures.

3. Dive into Reinforcement Learning#

Reinforcement learning (RL) is a domain where an agent learns to make decisions by interacting with an environment. OpenAI Gym offers standardized environments, making it an excellent place to test RL algorithms such as Q-learning or policy gradient methods.

4. Advanced Feature Engineering#

The more domain-specific and creative you get with feature engineering, the more likely you are to produce better models. Techniques like feature hashing for high-dimensional data and advanced transformations for time series (like difference transformations or rolling-window features) can drastically improve performance.

5. Contribute to Open Source#

There are many open-source software projects in the data science space. Contributing bug fixes, documentation, or new features to a project such as scikit-learn, Pandas, or PyTorch can boost both your confidence and reputation. You also get firsthand experience with large codebases and community-driven development processes.